<figure><img src="image_39.png" /><figcaption>Fig. 14 Naive Bayes classification results for other feature selection techniques (Part 1)</figcaption></figure>
<figure><img src="image_36.png" /><figcaption>Fig. 15 Naive Bayes classification results for other feature selection techniques (Part 2)</figcaption></figure>
<figure><img src="image_40.png" /><figcaption>Fig. 15 Naive Bayes classification results for other feature selection techniques (Part 2)</figcaption></figure>
<figure><img src="image_37.png" /><figcaption>Fig. 16 Random forest classification results for different feature selection techniques</figcaption></figure>

While we have only provided a detailed for the accuracy metric, identical observations
can be made for the other three metrics (precision, recall, and F1).

7.4.2 Random forest

The overall classification performance of the random forest classifier is much better than
the performance of the Naive Bayes classifier. To be more precise, the accuracy ranges from
0.8121 (Significance with 6171 features) to 0.6483 (Consistency). The accuracy of Sp-RST
is between 0.7733 and 0.7938 and again strictly increasing with the number of features per
partition, thus comparable to the best performing methods. Detailed results can be found in
Figs. 16, 17 and 18 and Table 12 in "Appendix A". We see that the overall ranking of the
methods tested is very similar to the Naive Bayes results and that Sp-RST outperforms/is
outperformed by roughly the same methods and parameterisations. The main exception is
that for methods where only parameter 6171 performed better in the case of Naive Bayes,
now also parameter 5566 produces slightly better results. However, the overall differences
are much smaller than in the case of the Naive Bayes classifier.
<figure><img src="image_41.png" /><figcaption>Fig. 16 Random forest classification results for different feature selection techniques</figcaption></figure>

While we have only provided a detailed for the accuracy metric, identical observations
can be made for the other three metrics (precision, recall, and F1).

7.4.2 Random forest

The overall classification performance of the random forest classifier is much better than
the performance of the Naive Bayes classifier. To be more precise, the accuracy ranges from
0.8121 (Significance with 6171 features) to 0.6483 (Consistency). The accuracy of Sp-RST
is between 0.7733 and 0.7938 and again strictly increasing with the number of features per
partition, thus comparable to the best performing methods. Detailed results can be found in
Figs. 16, 17 and 18 and Table 12 in "Appendix A". We see that the overall ranking of the
methods tested is very similar to the Naive Bayes results and that Sp-RST outperforms/is
outperformed by roughly the same methods and parameterisations. The main exception is
that for methods where only parameter 6171 performed better in the case of Naive Bayes,
now also parameter 5566 produces slightly better results. However, the overall differences
are much smaller than in the case of the Naive Bayes classifier.

<figure><img src="image_42.png" /></figure>
<figure><img src="image_38.png" /><figcaption>**Fig. 17 Random forest classification results for other feature selection techniques (Part 1)**</figcaption></figure>
<figure><img src="image_43.png" /><figcaption>Fig. 17 Random forest classification results for other feature selection techniques (Part 1)</figcaption></figure>
<figure><img src="image_39.png" /><figcaption>**Fig. 18 Random forest classification results for other feature selection techniques (Part 2)**</figcaption></figure>
<figure><img src="image_44.png" /><figcaption>**Fig. 18 Random forest classification results for other feature selection techniques (Part 2)**</figcaption></figure>
<figure><img src="image_40.png" /><figcaption>**Fig. 19** Runtime of the Naive Bayes classifier for the results of different feature selection techniques</figcaption></figure>

Again, we have executed pairwise Wilcoxon rank sum tests with Bonferroni correction to validate our results (see Tables 18, 19, 20, 21 in “Appendix B”). While most results for 4, 5 and 8 features per partition are statistically significant at confidence level 0.05, for quite a few comparisons with SP-RST (10) no statistical significance could be observed. This particularly holds for medium parameters such as 4117 and 5566 features, while in the case of the random forest classifier the parameter setting 6171 produces statistically different results in terms of accuracy. Recall that this was different for the Naive Bayes classifier.
<figure><img src="image_45.png" /><figcaption>**Fig. 19** Runtime of the Naive Bayes classifier for the results of different feature selection techniques</figcaption></figure>

Again, we have executed pairwise Wilcoxon rank sum tests with Bonferroni correction to validate our results (see Tables 18, 19, 20, 21 in “Appendix B”). While most results for 4, 5 and 8 features per partition are statistically significant at confidence level 0.05, for quite a few comparisons with SP-RST (10) no statistical significance could be observed. This particularly holds for medium parameters such as 4117 and 5566 features, while in the case of the random forest classifier the parameter setting 6171 produces statistically different results in terms of accuracy. Recall that this was different for the Naive Bayes classifier.
<figure><img src="image_41.png" /><figcaption>**Fig. 20** Runtime of the random forest classifier for the results of different feature selection techniques</figcaption></figure>

While we have only discussed the accuracy results in detail, identical observations can be made for the other three metrics shown in the graphs and tables.

### 7.4.3 Runtime comparison

We have provided a detailed analysis of the runtime for Sp-RST in Sect. 7.3. It should be noted that all other methods have negligible runtime for the feature selection part, and thus, there

<figure><img src="image_42.png" /></figure>
<figure><img src="image_46.png" /><figcaption>**Fig. 20** Runtime of the random forest classifier for the results of different feature selection techniques</figcaption></figure>

While we have only discussed the accuracy results in detail, identical observations can be made for the other three metrics shown in the graphs and tables.

### 7.4.3 Runtime comparison

We have provided a detailed analysis of the runtime for Sp-RST in Sect. 7.3. It should be noted that all other methods have negligible runtime for the feature selection part, and thus, there
is a clear trade-off between runtime and feature selection quality. Based on the comparable
classification results, we argue that the additional runtime is worthwhile, particularly if the
reduced feature set has the potential to be used repeatedly in different applications. We
summarize the runtime for the classification in Table 13 and depict boxplots in Figs. 19 and 20.

Besides these results, it is important to recall that as proved in [6], these existent methods
are inadequate to cope with a higher number of features as they will have scalability problems.
Hence, based on our detailed analysis and based on the results obtained from [6], we can
conclude that our proposed Sp-RST solution is a scalable and effective method for large-scale
data pre-processing. Thus, it is relevant to big data.

8 Conclusion and future work

In this paper, we have introduced a parallelized filter feature selection technique based on
rough set theory, called Sp-RST. We have presented a comprehensive experimental study to
investigate parameter settings and to demonstrate the stability and scalability of our proposed
method. Moreover, we have compared Sp-RST with other commonly used filter techniques.
We have used model evaluation using a Naive Bayes and a random forest classifier to evaluate
the quality of the feature set selected by Sp-RST and the other methods considered.

Our experiments show that the proposed method effectively performs feature selection
in a founded way without sacrificing performance. In terms of scalability, using a moderate
number of nodes yields a considerably improvement of the runtime and good speedup per-
formance. However, the improvement quickly stagnates if 8 or more nodes are used. Thus,
improving the speedup, sizeup and scaleup performance for more nodes is subject to future
work.

Our results show that Sp-RST is competitive or better against other methods on the Amazon
data set and only induces very small information loss: For the best Sp-RST parameter setting,
the classification accuracy for Naive Bayes is 6.22% smaller than for the best comparator
(Sum Squares), while for random forest it is only 1.83% (Significance). In comparison with the
original data set, we lose only 1.65% for Naive Bayes and 0.88% for random forest. Moreover,
we have demonstrated that Sp-RST is able to reliably identify the most and least important fea-
tures in the data set, which can be an important aspect when interpreting the feature selection
results from an application perspective. Improving the overall classification ratio of Sp-RST
is subject to future work. Particularly, we plan to investigate the performance and behavior
of Sp-RST on other larger data sets to further our understanding of its working principles.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

**Appendix A: Detailed classification and runtime results**

See Tables 11, 12 and 13.
is a clear trade-off between runtime and feature selection quality. Based on the comparable
classification results, we argue that the additional runtime is worthwhile, particularly if the
reduced feature set has the potential to be used repeatedly in different applications. We
summarize the runtime for the classification in Table 13 and depict boxplots in Figs. 19 and 20.

Besides these results, it is important to recall that as proved in [6], these existent methods
are inadequate to cope with a higher number of features as they will have scalability problems.
Hence, based on our detailed analysis and based on the results obtained from [6], we can
conclude that our proposed Sp-RST solution is a scalable and effective method for large-scale
data pre-processing. Thus, it is relevant to big data.

8 Conclusion and future work

In this paper, we have introduced a parallelized filter feature selection technique based on
rough set theory, called Sp-RST. We have presented a comprehensive experimental study to
investigate parameter settings and to demonstrate the stability and scalability of our proposed
method. Moreover, we have compared Sp-RST with other commonly used filter techniques.
We have used model evaluation using a Naive Bayes and a random forest classifier to evaluate
the quality of the feature set selected by Sp-RST and the other methods considered.

Our experiments show that the proposed method effectively performs feature selection
in a founded way without sacrificing performance. In terms of scalability, using a moderate
number of nodes yields a considerably improvement of the runtime and good speedup per-
formance. However, the improvement quickly stagnates if 8 or more nodes are used. Thus,
improving the speedup, sizeup and scaleup performance for more nodes is subject to future
work.

Our results show that Sp-RST is competitive or better against other methods on the Amazon
data set and only induces very small information loss: For the best Sp-RST parameter setting,
the classification accuracy for Naive Bayes is 6.22% smaller than for the best comparator
(Sum Squares), while for random forest it is only 1.83% (Significance). In comparison with the
original data set, we lose only 1.65% for Naive Bayes and 0.88% for random forest. Moreover,
we have demonstrated that Sp-RST is able to reliably identify the most and least important fea-
tures in the data set, which can be an important aspect when interpreting the feature selection
results from an application perspective. Improving the overall classification ratio of Sp-RST
is subject to future work. Particularly, we plan to investigate the performance and behavior
of Sp-RST on other larger data sets to further our understanding of its working principles.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

**Appendix A: Detailed classification and runtime results**

See Tables 11, 12 and 13.
<table><caption>**Table 11** Naive Bayes classification results for different feature selection techniques</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Original</td><td>10,000<br>(-)</td><td>0.6099<br>(0.0080)</td><td>0.6451<br>(0.0082)</td><td>0.6098<br>(0.0080)</td><td>0.6118<br>(0.0087)</td></tr><tr><td>CV (t = 0)</td><td>4149<br>(-)</td><td>0.5152<br>(0.0067)</td><td>0.5703<br>(0.0082)</td><td>0.5152<br>(0.0068)</td><td>0.5213<br>(0.0067)</td></tr><tr><td>CV (3205)</td><td>3205<br>(-)</td><td>0.4877<br>(0.0090)</td><td>0.5387<br>(0.0094)</td><td>0.4878<br>(0.0089)</td><td>0.4925<br>(0.0094)</td></tr><tr><td>CV (4117)</td><td>4117<br>(-)</td><td>0.5153<br>(0.0074)</td><td>0.5701<br>(0.0093)</td><td>0.5153<br>(0.0075)</td><td>0.5213<br>(0.0074)</td></tr><tr><td>CV (5566)</td><td>5566<br>(-)</td><td>0.5431<br>(0.0069)</td><td>0.5906<br>(0.0075)</td><td>0.5432<br>(0.0070)</td><td>0.5475<br>(0.0067)</td></tr><tr><td>CV (6171)</td><td>6171<br>(-)</td><td>0.5477<br>(0.0072)</td><td>0.5933<br>(0.0082)</td><td>0.5478<br>(0.0072)</td><td>0.5522<br>(0.0072)</td></tr><tr><td>Cfs (Greedy)</td><td>41<br>(-)</td><td>0.6083<br>(0.0039)</td><td>0.6156<br>(0.0049)</td><td>0.6084<br>(0.0039)</td><td>0.6053<br>(0.0043)</td></tr><tr><td>Chi squared (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr><tr><td>Chi squared (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Chi squared (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Chi squared (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Chi squared (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6295<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr><tr><td>Consistency (Greedy)</td><td>30<br>(-)</td><td>0.5443<br>(0.0044)</td><td>0.5514<br>(0.0050)</td><td>0.5443<br>(0.0045)</td><td>0.5391<br>(0.0044)</td></tr><tr><td>Correlation (t = 0)</td><td>10,000<br>(-)</td><td>0.6147<br>(0.0059)</td><td>0.6512<br>(0.0063)</td><td>0.6147<br>(0.0059)</td><td>0.6170<br>(0.0069)</td></tr><tr><td>Correlation (3205)</td><td>3205<br>(-)</td><td>0.6232<br>(0.0048)</td><td>0.6588<br>(0.0065)</td><td>0.6231<br>(0.0048)</td><td>0.6266<br>(0.0056)</td></tr><tr><td>Correlation (4117)</td><td>4117<br>(-)</td><td>0.6207<br>(0.0049)</td><td>0.6570<br>(0.0077)</td><td>0.6208<br>(0.0049)</td><td>0.6234<br>(0.0058)</td></tr><tr><td>Correlation (5566)</td><td>5566<br>(-)</td><td>0.6145<br>(0.0061)</td><td>0.6519<br>(0.0079)</td><td>0.6143<br>(0.0061)</td><td>0.6176<br>(0.0070)</td></tr><tr><td>Correlation (6171)</td><td>6171<br>(-)</td><td>0.6187<br>(0.0063)</td><td>0.6574<br>(0.0065)</td><td>0.6189<br>(0.0063)</td><td>0.6225<br>(0.0064)</td></tr><tr><td>gain ratio (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr></tbody></table>
<table><caption>**Table 11** Naive Bayes classification results for different feature selection techniques</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Original</td><td>10,000<br>(-)</td><td>0.6099<br>(0.0080)</td><td>0.6451<br>(0.0082)</td><td>0.6098<br>(0.0080)</td><td>0.6118<br>(0.0087)</td></tr><tr><td>CV (t = 0)</td><td>4149<br>(-)</td><td>0.5152<br>(0.0067)</td><td>0.5703<br>(0.0082)</td><td>0.5152<br>(0.0068)</td><td>0.5213<br>(0.0067)</td></tr><tr><td>CV (3205)</td><td>3205<br>(-)</td><td>0.4877<br>(0.0090)</td><td>0.5387<br>(0.0094)</td><td>0.4878<br>(0.0089)</td><td>0.4925<br>(0.0094)</td></tr><tr><td>CV (4117)</td><td>4117<br>(-)</td><td>0.5153<br>(0.0074)</td><td>0.5701<br>(0.0093)</td><td>0.5153<br>(0.0075)</td><td>0.5213<br>(0.0074)</td></tr><tr><td>CV (5566)</td><td>5566<br>(-)</td><td>0.5431<br>(0.0069)</td><td>0.5906<br>(0.0075)</td><td>0.5432<br>(0.0070)</td><td>0.5475<br>(0.0067)</td></tr><tr><td>CV (6171)</td><td>6171<br>(-)</td><td>0.5477<br>(0.0072)</td><td>0.5933<br>(0.0082)</td><td>0.5478<br>(0.0072)</td><td>0.5522<br>(0.0072)</td></tr><tr><td>Cfs (Greedy)</td><td>41<br>(-)</td><td>0.6083<br>(0.0039)</td><td>0.6156<br>(0.0049)</td><td>0.6084<br>(0.0039)</td><td>0.6053<br>(0.0043)</td></tr><tr><td>Chi squared (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr><tr><td>Chi squared (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Chi squared (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Chi squared (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Chi squared (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6295<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr><tr><td>Consistency (Greedy)</td><td>30<br>(-)</td><td>0.5443<br>(0.0044)</td><td>0.5514<br>(0.0050)</td><td>0.5443<br>(0.0045)</td><td>0.5391<br>(0.0044)</td></tr><tr><td>Correlation (t = 0)</td><td>10,000<br>(-)</td><td>0.6147<br>(0.0059)</td><td>0.6512<br>(0.0063)</td><td>0.6147<br>(0.0059)</td><td>0.6170<br>(0.0069)</td></tr><tr><td>Correlation (3205)</td><td>3205<br>(-)</td><td>0.6232<br>(0.0048)</td><td>0.6588<br>(0.0065)</td><td>0.6231<br>(0.0048)</td><td>0.6266<br>(0.0056)</td></tr><tr><td>Correlation (4117)</td><td>4117<br>(-)</td><td>0.6207<br>(0.0049)</td><td>0.6570<br>(0.0077)</td><td>0.6208<br>(0.0049)</td><td>0.6234<br>(0.0058)</td></tr><tr><td>Correlation (5566)</td><td>5566<br>(-)</td><td>0.6145<br>(0.0061)</td><td>0.6519<br>(0.0079)</td><td>0.6143<br>(0.0061)</td><td>0.6176<br>(0.0070)</td></tr><tr><td>Correlation (6171)</td><td>6171<br>(-)</td><td>0.6187<br>(0.0063)</td><td>0.6574<br>(0.0065)</td><td>0.6189<br>(0.0063)</td><td>0.6225<br>(0.0064)</td></tr><tr><td>gain ratio (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr></tbody></table>
<table><caption>**Table 11** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>gain ratio (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>gain ratio (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>gain ratio (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>gain ratio (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6295<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr><tr><td>Info gain (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr><tr><td>Info gain (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Info gain (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Info gain (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Info gain (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6294<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr><tr><td>ReliefF (t = 0)</td><td>7957<br>(-)</td><td>0.6087<br>(0.0061)</td><td>0.6476<br>(0.0069)</td><td>0.6087<br>(0.0060)</td><td>0.6120<br>(0.0067)</td></tr><tr><td>ReliefF (3205)</td><td>3205<br>(-)</td><td>0.6008<br>(0.0076)</td><td>0.6367<br>(0.0074)</td><td>0.6007<br>(0.0076)</td><td>0.6044<br>(0.0075)</td></tr><tr><td>ReliefF (4117)</td><td>4117<br>(-)</td><td>0.5972<br>(0.0060)</td><td>0.6353<br>(0.0068)</td><td>0.5973<br>(0.0061)</td><td>0.6010<br>(0.0065)</td></tr><tr><td>ReliefF (5566)</td><td>5566<br>(-)</td><td>0.6087<br>(0.0062)</td><td>0.6497<br>(0.0088)</td><td>0.6086<br>(0.0062)</td><td>0.6121<br>(0.0069)</td></tr><tr><td>ReliefF (6171)</td><td>6171<br>(-)</td><td>0.6093<br>(0.0053)</td><td>0.6479<br>(0.0076)</td><td>0.6093<br>(0.0055)</td><td>0.6132<br>(0.0065)</td></tr><tr><td>Significance (t = 0)</td><td>10,000<br>(-)</td><td>0.6139<br>(0.0065)</td><td>0.6475<br>(0.0065)</td><td>0.6138<br>(0.0066)</td><td>0.6151<br>(0.0068)</td></tr><tr><td>Significance (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Significance (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Significance (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Significance (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6295<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr></tbody></table>
<table><caption>**Table 11** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>gain ratio (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>gain ratio (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>gain ratio (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>gain ratio (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6295<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr><tr><td>Info gain (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr><tr><td>Info gain (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Info gain (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Info gain (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Info gain (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6294<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr><tr><td>ReliefF (t = 0)</td><td>7957<br>(-)</td><td>0.6087<br>(0.0061)</td><td>0.6476<br>(0.0069)</td><td>0.6087<br>(0.0060)</td><td>0.6120<br>(0.0067)</td></tr><tr><td>ReliefF (3205)</td><td>3205<br>(-)</td><td>0.6008<br>(0.0076)</td><td>0.6367<br>(0.0074)</td><td>0.6007<br>(0.0076)</td><td>0.6044<br>(0.0075)</td></tr><tr><td>ReliefF (4117)</td><td>4117<br>(-)</td><td>0.5972<br>(0.0060)</td><td>0.6353<br>(0.0068)</td><td>0.5973<br>(0.0061)</td><td>0.6010<br>(0.0065)</td></tr><tr><td>ReliefF (5566)</td><td>5566<br>(-)</td><td>0.6087<br>(0.0062)</td><td>0.6497<br>(0.0088)</td><td>0.6086<br>(0.0062)</td><td>0.6121<br>(0.0069)</td></tr><tr><td>ReliefF (6171)</td><td>6171<br>(-)</td><td>0.6093<br>(0.0053)</td><td>0.6479<br>(0.0076)</td><td>0.6093<br>(0.0055)</td><td>0.6132<br>(0.0065)</td></tr><tr><td>Significance (t = 0)</td><td>10,000<br>(-)</td><td>0.6139<br>(0.0065)</td><td>0.6475<br>(0.0065)</td><td>0.6138<br>(0.0066)</td><td>0.6151<br>(0.0068)</td></tr><tr><td>Significance (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Significance (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Significance (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Significance (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6295<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr></tbody></table>
<table><caption>**Table 11** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>SpRST (4)</td><td>3205.333<br>(24.35296)</td><td>0.5634<br>(0.0061)</td><td>0.6050<br>(0.0073)</td><td>0.5635<br>(0.0061)</td><td>0.5681<br>(0.0068)</td></tr><tr><td>SpRST (5)</td><td>4117.000<br>(18.95257)</td><td>0.5736<br>(0.0058)</td><td>0.6156<br>(0.0071)</td><td>0.5736<br>(0.0058)</td><td>0.5772<br>(0.0061)</td></tr><tr><td>SpRST (8)</td><td>5566.000<br>(23.89142)</td><td>0.5893<br>(0.0066)</td><td>0.6304<br>(0.0067)</td><td>0.5893<br>(0.0066)</td><td>0.5929<br>(0.0067)</td></tr><tr><td>SpRST (10)</td><td>6171.333<br>(26.01282)</td><td>0.5934<br>(0.0067)</td><td>0.6321<br>(0.0065)</td><td>0.5934<br>(0.0067)</td><td>0.5966<br>(0.0069)</td></tr><tr><td>SumSquaresRatio (3205)</td><td>3205<br>(-)</td><td>0.6498<br>(0.0031)</td><td>0.6740<br>(0.0033)</td><td>0.6497<br>(0.0029)</td><td>0.6510<br>(0.0035)</td></tr><tr><td>SumSquaresRatio (4117)</td><td>4117<br>(-)</td><td>0.6556<br>(0.0074)</td><td>0.6824<br>(0.0070)</td><td>0.6557<br>(0.0073)</td><td>0.6558<br>(0.0077)</td></tr><tr><td>SumSquaresRatio (5566)</td><td>5566<br>(-)</td><td>0.6507<br>(0.0045)</td><td>0.6794<br>(0.0061)</td><td>0.6507<br>(0.0045)</td><td>0.6522<br>(0.0049)</td></tr><tr><td>SumSquaresRatio (6171)</td><td>6171<br>(-)</td><td>0.6511<br>(0.0056)</td><td>0.6825<br>(0.0059)</td><td>0.6510<br>(0.0055)</td><td>0.6536<br>(0.0059)</td></tr><tr><td>Symmetrical uncert (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr><tr><td>Symmetrical uncert (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Symmetrical uncert (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Symmetrical uncert (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Symmetrical uncert (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6294<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr></tbody></table>

Averages and standard deviation in brackets
<table><caption>**Table 11** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>SpRST (4)</td><td>3205.333<br>(24.35296)</td><td>0.5634<br>(0.0061)</td><td>0.6050<br>(0.0073)</td><td>0.5635<br>(0.0061)</td><td>0.5681<br>(0.0068)</td></tr><tr><td>SpRST (5)</td><td>4117.000<br>(18.95257)</td><td>0.5736<br>(0.0058)</td><td>0.6156<br>(0.0071)</td><td>0.5736<br>(0.0058)</td><td>0.5772<br>(0.0061)</td></tr><tr><td>SpRST (8)</td><td>5566.000<br>(23.89142)</td><td>0.5893<br>(0.0066)</td><td>0.6304<br>(0.0067)</td><td>0.5893<br>(0.0066)</td><td>0.5929<br>(0.0067)</td></tr><tr><td>SpRST (10)</td><td>6171.333<br>(26.01282)</td><td>0.5934<br>(0.0067)</td><td>0.6321<br>(0.0065)</td><td>0.5934<br>(0.0067)</td><td>0.5966<br>(0.0069)</td></tr><tr><td>SumSquaresRatio (3205)</td><td>3205<br>(-)</td><td>0.6498<br>(0.0031)</td><td>0.6740<br>(0.0033)</td><td>0.6497<br>(0.0029)</td><td>0.6510<br>(0.0035)</td></tr><tr><td>SumSquaresRatio (4117)</td><td>4117<br>(-)</td><td>0.6556<br>(0.0074)</td><td>0.6824<br>(0.0070)</td><td>0.6557<br>(0.0073)</td><td>0.6558<br>(0.0077)</td></tr><tr><td>SumSquaresRatio (5566)</td><td>5566<br>(-)</td><td>0.6507<br>(0.0045)</td><td>0.6794<br>(0.0061)</td><td>0.6507<br>(0.0045)</td><td>0.6522<br>(0.0049)</td></tr><tr><td>SumSquaresRatio (6171)</td><td>6171<br>(-)</td><td>0.6511<br>(0.0056)</td><td>0.6825<br>(0.0059)</td><td>0.6510<br>(0.0055)</td><td>0.6536<br>(0.0059)</td></tr><tr><td>Symmetrical uncert (t = 0)</td><td>113<br>(-)</td><td>0.5771<br>(0.0039)</td><td>0.5870<br>(0.0055)</td><td>0.5771<br>(0.0038)</td><td>0.5705<br>(0.0043)</td></tr><tr><td>Symmetrical uncert (3205)</td><td>3205<br>(-)</td><td>0.5103<br>(0.0057)</td><td>0.5340<br>(0.0071)</td><td>0.5102<br>(0.0058)</td><td>0.5123<br>(0.0059)</td></tr><tr><td>Symmetrical uncert (4117)</td><td>4117<br>(-)</td><td>0.5595<br>(0.0057)</td><td>0.5823<br>(0.0051)</td><td>0.5596<br>(0.0058)</td><td>0.5580<br>(0.0053)</td></tr><tr><td>Symmetrical uncert (5566)</td><td>5566<br>(-)</td><td>0.5863<br>(0.0038)</td><td>0.6190<br>(0.0047)</td><td>0.5865<br>(0.0037)</td><td>0.5872<br>(0.0041)</td></tr><tr><td>Symmetrical uncert (6171)</td><td>6171<br>(-)</td><td>0.5965<br>(0.0042)</td><td>0.6294<br>(0.0041)</td><td>0.5966<br>(0.0042)</td><td>0.5976<br>(0.0042)</td></tr></tbody></table>

Averages and standard deviation in brackets
<table><caption>**Table 12 Random forest classification results for different feature selection techniques**</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Original</td><td>10,000<br>(-)</td><td>0.8026<br>(0.0038)</td><td>0.8174<br>(0.0042)</td><td>0.8026<br>(0.0038)</td><td>0.7963<br>(0.0044)</td></tr><tr><td>CV (t = 0)</td><td>4149<br>(-)</td><td>0.6855<br>(0.0036)</td><td>0.7264<br>(0.0070)</td><td>0.6855<br>(0.0036)</td><td>0.6766<br>(0.0040)</td></tr><tr><td>CV (3205)</td><td>3205<br>(-)</td><td>0.6503<br>(0.0045)</td><td>0.7029<br>(0.0060)</td><td>0.6503<br>(0.0045)</td><td>0.6402<br>(0.0052)</td></tr><tr><td>CV (4117)</td><td>4117<br>(-)</td><td>0.6789<br>(0.0042)</td><td>0.7224<br>(0.0079)</td><td>0.6789<br>(0.0042)</td><td>0.6698<br>(0.0043)</td></tr><tr><td>CV (5566)</td><td>5566<br>(-)</td><td>0.7337<br>(0.0031)</td><td>0.7621<br>(0.0033)</td><td>0.7337<br>(0.0031)</td><td>0.7265<br>(0.0038)</td></tr><tr><td>CV (6171)</td><td>6171<br>(-)</td><td>0.7451<br>(0.0050)</td><td>0.7740<br>(0.0051)</td><td>0.7451<br>(0.0050)</td><td>0.7381<br>(0.0050)</td></tr><tr><td>Cfs (Greedy)</td><td>41<br>(-)</td><td>0.6881<br>(0.0019)</td><td>0.6881<br>(0.0027)</td><td>0.6881<br>(0.0019)</td><td>0.6786<br>(0.0022)</td></tr><tr><td>Chi squared (t = 0)</td><td>113<br>(-)</td><td>0.7107<br>(0.0029)</td><td>0.7171<br>(0.0033)</td><td>0.7107<br>(0.0029)</td><td>0.7070<br>(0.0029)</td></tr><tr><td>Chi squared (3205)</td><td>3205<br>(-)</td><td>0.7607<br>(0.0029)</td><td>0.7717<br>(0.0042)</td><td>0.7607<br>(0.0029)</td><td>0.7513<br>(0.0029)</td></tr><tr><td>Chi squared (4117)</td><td>4117<br>(-)</td><td>0.7927<br>(0.0036)</td><td>0.8054<br>(0.0038)</td><td>0.7927<br>(0.0036)</td><td>0.7834<br>(0.0039)</td></tr><tr><td>Chi squared (5566)</td><td>5566<br>(-)</td><td>0.8002<br>(0.0038)</td><td>0.8145<br>(0.0044)</td><td>0.8002<br>(0.0038)</td><td>0.7919<br>(0.0041)</td></tr><tr><td>Chi squared (6171)</td><td>6171<br>(-)</td><td>0.8073<br>(0.0058)</td><td>0.8225<br>(0.0059)</td><td>0.8073<br>(0.0058)</td><td>0.7998<br>(0.0062)</td></tr><tr><td>Consistency (Greedy)</td><td>30<br>(-)</td><td>0.6483<br>(0.0033)</td><td>0.6489<br>(0.0047)</td><td>0.6483<br>(0.0033)</td><td>0.6383<br>(0.0035)</td></tr><tr><td>Correlation (t = 0)</td><td>10,000<br>(-)</td><td>0.8016<br>(0.0064)</td><td>0.8178<br>(0.0081)</td><td>0.8016<br>(0.0064)</td><td>0.7949<br>(0.0068)</td></tr><tr><td>Correlation (3205)</td><td>3205<br>(-)</td><td>0.7930<br>(0.0032)</td><td>0.8073<br>(0.0049)</td><td>0.7930<br>(0.0032)</td><td>0.7860<br>(0.0036)</td></tr><tr><td>Correlation (4117)</td><td>4117<br>(-)</td><td>0.7979<br>(0.0054)</td><td>0.8134<br>(0.0054)</td><td>0.7979<br>(0.0054)</td><td>0.7909<br>(0.0060)</td></tr><tr><td>Correlation (5566)</td><td>5566<br>(-)</td><td>0.8017<br>(0.0040)</td><td>0.8169<br>(0.0041)</td><td>0.8017<br>(0.0040)</td><td>0.7949<br>(0.0041)</td></tr><tr><td>Correlation (6171)</td><td>6171<br>(-)</td><td>0.8029<br>(0.0049)</td><td>0.8186<br>(0.0041)</td><td>0.8029<br>(0.0049)</td><td>0.7966<br>(0.0054)</td></tr><tr><td>Gain ratio (t = 0)</td><td>113<br>(-)</td><td>0.7122<br>(0.0024)</td><td>0.7178<br>(0.0029)</td><td>0.7122<br>(0.0024)</td><td>0.7083<br>(0.0027)</td></tr><tr><td>Gain ratio (3205)</td><td>3205<br>(-)</td><td>0.7586<br>(0.0054)</td><td>0.7681<br>(0.0064)</td><td>0.7586<br>(0.0054)</td><td>0.7487<br>(0.0055)</td></tr></tbody></table>
<table><caption>**Table 12 Random forest classification results for different feature selection techniques**</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Original</td><td>10,000<br>(-)</td><td>0.8026<br>(0.0038)</td><td>0.8174<br>(0.0042)</td><td>0.8026<br>(0.0038)</td><td>0.7963<br>(0.0044)</td></tr><tr><td>CV (t = 0)</td><td>4149<br>(-)</td><td>0.6855<br>(0.0036)</td><td>0.7264<br>(0.0070)</td><td>0.6855<br>(0.0036)</td><td>0.6766<br>(0.0040)</td></tr><tr><td>CV (3205)</td><td>3205<br>(-)</td><td>0.6503<br>(0.0045)</td><td>0.7029<br>(0.0060)</td><td>0.6503<br>(0.0045)</td><td>0.6402<br>(0.0052)</td></tr><tr><td>CV (4117)</td><td>4117<br>(-)</td><td>0.6789<br>(0.0042)</td><td>0.7224<br>(0.0079)</td><td>0.6789<br>(0.0042)</td><td>0.6698<br>(0.0043)</td></tr><tr><td>CV (5566)</td><td>5566<br>(-)</td><td>0.7337<br>(0.0031)</td><td>0.7621<br>(0.0033)</td><td>0.7337<br>(0.0031)</td><td>0.7265<br>(0.0038)</td></tr><tr><td>CV (6171)</td><td>6171<br>(-)</td><td>0.7451<br>(0.0050)</td><td>0.7740<br>(0.0051)</td><td>0.7451<br>(0.0050)</td><td>0.7381<br>(0.0050)</td></tr><tr><td>Cfs (Greedy)</td><td>41<br>(-)</td><td>0.6881<br>(0.0019)</td><td>0.6881<br>(0.0027)</td><td>0.6881<br>(0.0019)</td><td>0.6786<br>(0.0022)</td></tr><tr><td>Chi squared (t = 0)</td><td>113<br>(-)</td><td>0.7107<br>(0.0029)</td><td>0.7171<br>(0.0033)</td><td>0.7107<br>(0.0029)</td><td>0.7070<br>(0.0029)</td></tr><tr><td>Chi squared (3205)</td><td>3205<br>(-)</td><td>0.7607<br>(0.0029)</td><td>0.7717<br>(0.0042)</td><td>0.7607<br>(0.0029)</td><td>0.7513<br>(0.0029)</td></tr><tr><td>Chi squared (4117)</td><td>4117<br>(-)</td><td>0.7927<br>(0.0036)</td><td>0.8054<br>(0.0038)</td><td>0.7927<br>(0.0036)</td><td>0.7834<br>(0.0039)</td></tr><tr><td>Chi squared (5566)</td><td>5566<br>(-)</td><td>0.8002<br>(0.0038)</td><td>0.8145<br>(0.0044)</td><td>0.8002<br>(0.0038)</td><td>0.7919<br>(0.0041)</td></tr><tr><td>Chi squared (6171)</td><td>6171<br>(-)</td><td>0.8073<br>(0.0058)</td><td>0.8225<br>(0.0059)</td><td>0.8073<br>(0.0058)</td><td>0.7998<br>(0.0062)</td></tr><tr><td>Consistency (Greedy)</td><td>30<br>(-)</td><td>0.6483<br>(0.0033)</td><td>0.6489<br>(0.0047)</td><td>0.6483<br>(0.0033)</td><td>0.6383<br>(0.0035)</td></tr><tr><td>Correlation (t = 0)</td><td>10,000<br>(-)</td><td>0.8016<br>(0.0064)</td><td>0.8178<br>(0.0081)</td><td>0.8016<br>(0.0064)</td><td>0.7949<br>(0.0068)</td></tr><tr><td>Correlation (3205)</td><td>3205<br>(-)</td><td>0.7930<br>(0.0032)</td><td>0.8073<br>(0.0049)</td><td>0.7930<br>(0.0032)</td><td>0.7860<br>(0.0036)</td></tr><tr><td>Correlation (4117)</td><td>4117<br>(-)</td><td>0.7979<br>(0.0054)</td><td>0.8134<br>(0.0054)</td><td>0.7979<br>(0.0054)</td><td>0.7909<br>(0.0060)</td></tr><tr><td>Correlation (5566)</td><td>5566<br>(-)</td><td>0.8017<br>(0.0040)</td><td>0.8169<br>(0.0041)</td><td>0.8017<br>(0.0040)</td><td>0.7949<br>(0.0041)</td></tr><tr><td>Correlation (6171)</td><td>6171<br>(-)</td><td>0.8029<br>(0.0049)</td><td>0.8186<br>(0.0041)</td><td>0.8029<br>(0.0049)</td><td>0.7966<br>(0.0054)</td></tr><tr><td>Gain ratio (t = 0)</td><td>113<br>(-)</td><td>0.7122<br>(0.0024)</td><td>0.7178<br>(0.0029)</td><td>0.7122<br>(0.0024)</td><td>0.7083<br>(0.0027)</td></tr><tr><td>Gain ratio (3205)</td><td>3205<br>(-)</td><td>0.7586<br>(0.0054)</td><td>0.7681<br>(0.0064)</td><td>0.7586<br>(0.0054)</td><td>0.7487<br>(0.0055)</td></tr></tbody></table>
<table><caption>**Table 12** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Gain ratio (4117)</td><td>4117<br>(-)</td><td>0.7927<br>(0.0038)</td><td>0.8065<br>(0.0034)</td><td>0.7927<br>(0.0038)</td><td>0.7837<br>(0.0040)</td></tr><tr><td>Gain ratio (5566)</td><td>5566<br>(-)</td><td>0.8021<br>(0.0042)</td><td>0.8158<br>(0.0048)</td><td>0.8021<br>(0.0042)</td><td>0.7929<br>(0.0047)</td></tr><tr><td>Gain ratio (6171)</td><td>6171<br>(-)</td><td>0.8066<br>(0.0057)</td><td>0.8211<br>(0.0063)</td><td>0.8066<br>(0.0057)</td><td>0.7985<br>(0.0058)</td></tr><tr><td>Info gain (t = 0)</td><td>113<br>(-)</td><td>0.7097<br>(0.0044)</td><td>0.7159<br>(0.0042)</td><td>0.7097<br>(0.0044)</td><td>0.7058<br>(0.0046)</td></tr><tr><td>Info gain (3205)</td><td>3205<br>(-)</td><td>0.7621<br>(0.0048)</td><td>0.7725<br>(0.0067)</td><td>0.7621<br>(0.0048)</td><td>0.7524<br>(0.0048)</td></tr><tr><td>Info gain (4117)</td><td>4117<br>(-)</td><td>0.7921<br>(0.0033)</td><td>0.8067<br>(0.0040)</td><td>0.7921<br>(0.0033)</td><td>0.7823<br>(0.0037)</td></tr><tr><td>Info gain (5566)</td><td>5566<br>(-)</td><td>0.8007<br>(0.0039)</td><td>0.8151<br>(0.0054)</td><td>0.8007<br>(0.0039)</td><td>0.7913<br>(0.0044)</td></tr><tr><td>Info gain (6171)</td><td>6171<br>(-)</td><td>0.8093<br>(0.0058)</td><td>0.8231<br>(0.0064)</td><td>0.8093<br>(0.0058)</td><td>0.8017<br>(0.0063)</td></tr><tr><td>ReliefF (t = 0)</td><td>7957<br>(-)</td><td>0.8051<br>(0.0051)</td><td>0.8210<br>(0.0047)</td><td>0.8051<br>(0.0051)</td><td>0.7989<br>(0.0060)</td></tr><tr><td>ReliefF (3205)</td><td>3205<br>(-)</td><td>0.7959<br>(0.0051)</td><td>0.8106<br>(0.0064)</td><td>0.7959<br>(0.0051)</td><td>0.7891<br>(0.0059)</td></tr><tr><td>ReliefF (4117)</td><td>4117<br>(-)</td><td>0.8000<br>(0.0039)</td><td>0.8133<br>(0.0037)</td><td>0.8000<br>(0.0039)</td><td>0.7935<br>(0.0040)</td></tr><tr><td>ReliefF (5566)</td><td>5566<br>(-)</td><td>0.8017<br>(0.0056)</td><td>0.8172<br>(0.0071)</td><td>0.8017<br>(0.0056)</td><td>0.7949<br>(0.0058)</td></tr><tr><td>ReliefF (6171)</td><td>6171<br>(-)</td><td>0.8015<br>(0.0061)</td><td>0.8186<br>(0.0066)</td><td>0.8015<br>(0.0061)</td><td>0.7951<br>(0.0066)</td></tr><tr><td>Significance (t = 0)</td><td>10,000<br>(-)</td><td>0.7998<br>(0.0070)</td><td>0.8170<br>(0.0079)</td><td>0.7998<br>(0.0070)</td><td>0.7933<br>(0.0078)</td></tr><tr><td>Significance (3205)</td><td>3205<br>(-)</td><td>0.7587<br>(0.0035)</td><td>0.7682<br>(0.0060)</td><td>0.7587<br>(0.0035)</td><td>0.7490<br>(0.0039)</td></tr><tr><td>Significance (4117)</td><td>4117<br>(-)</td><td>0.7952<br>(0.0052)</td><td>0.8076<br>(0.0040)</td><td>0.7952<br>(0.0052)</td><td>0.7860<br>(0.0053)</td></tr><tr><td>Significance (5566)</td><td>5566<br>(-)</td><td>0.8043<br>(0.0040)</td><td>0.8187<br>(0.0052)</td><td>0.8043<br>(0.0040)</td><td>0.7960<br>(0.0049)</td></tr><tr><td>Significance (6171)</td><td>6171<br>(-)</td><td>0.8121<br>(0.0042)</td><td>0.8258<br>(0.0037)</td><td>0.8121<br>(0.0042)</td><td>0.8047<br>(0.0042)</td></tr><tr><td>SpRST (4)</td><td>3205.333<br>(24.35296)</td><td>0.7733<br>(0.0058)</td><td>0.7878<br>(0.0062)</td><td>0.7733<br>(0.0058)</td><td>0.7652<br>(0.0059)</td></tr><tr><td>SpRST (5)</td><td>4117.000<br>(18.95257)</td><td>0.7823<br>(0.0065)</td><td>0.7980<br>(0.0072)</td><td>0.7823<br>(0.0065)</td><td>0.7743<br>(0.0070)</td></tr><tr><td>SpRST (8)</td><td>5566.000<br>(23.89142)</td><td>0.7931<br>(0.0060)</td><td>0.8092<br>(0.0053)</td><td>0.7931<br>(0.0060)</td><td>0.7861<br>(0.0063)</td></tr></tbody></table>
<table><caption>**Table 12** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Gain ratio (4117)</td><td>4117<br>(-)</td><td>0.7927<br>(0.0038)</td><td>0.8065<br>(0.0034)</td><td>0.7927<br>(0.0038)</td><td>0.7837<br>(0.0040)</td></tr><tr><td>Gain ratio (5566)</td><td>5566<br>(-)</td><td>0.8021<br>(0.0042)</td><td>0.8158<br>(0.0048)</td><td>0.8021<br>(0.0042)</td><td>0.7929<br>(0.0047)</td></tr><tr><td>Gain ratio (6171)</td><td>6171<br>(-)</td><td>0.8066<br>(0.0057)</td><td>0.8211<br>(0.0063)</td><td>0.8066<br>(0.0057)</td><td>0.7985<br>(0.0058)</td></tr><tr><td>Info gain (t = 0)</td><td>113<br>(-)</td><td>0.7097<br>(0.0044)</td><td>0.7159<br>(0.0042)</td><td>0.7097<br>(0.0044)</td><td>0.7058<br>(0.0046)</td></tr><tr><td>Info gain (3205)</td><td>3205<br>(-)</td><td>0.7621<br>(0.0048)</td><td>0.7725<br>(0.0067)</td><td>0.7621<br>(0.0048)</td><td>0.7524<br>(0.0048)</td></tr><tr><td>Info gain (4117)</td><td>4117<br>(-)</td><td>0.7921<br>(0.0033)</td><td>0.8067<br>(0.0040)</td><td>0.7921<br>(0.0033)</td><td>0.7823<br>(0.0037)</td></tr><tr><td>Info gain (5566)</td><td>5566<br>(-)</td><td>0.8007<br>(0.0039)</td><td>0.8151<br>(0.0054)</td><td>0.8007<br>(0.0039)</td><td>0.7913<br>(0.0044)</td></tr><tr><td>Info gain (6171)</td><td>6171<br>(-)</td><td>0.8093<br>(0.0058)</td><td>0.8231<br>(0.0064)</td><td>0.8093<br>(0.0058)</td><td>0.8017<br>(0.0063)</td></tr><tr><td>ReliefF (t = 0)</td><td>7957<br>(-)</td><td>0.8051<br>(0.0051)</td><td>0.8210<br>(0.0047)</td><td>0.8051<br>(0.0051)</td><td>0.7989<br>(0.0060)</td></tr><tr><td>ReliefF (3205)</td><td>3205<br>(-)</td><td>0.7959<br>(0.0051)</td><td>0.8106<br>(0.0064)</td><td>0.7959<br>(0.0051)</td><td>0.7891<br>(0.0059)</td></tr><tr><td>ReliefF (4117)</td><td>4117<br>(-)</td><td>0.8000<br>(0.0039)</td><td>0.8133<br>(0.0037)</td><td>0.8000<br>(0.0039)</td><td>0.7935<br>(0.0040)</td></tr><tr><td>ReliefF (5566)</td><td>5566<br>(-)</td><td>0.8017<br>(0.0056)</td><td>0.8172<br>(0.0071)</td><td>0.8017<br>(0.0056)</td><td>0.7949<br>(0.0058)</td></tr><tr><td>ReliefF (6171)</td><td>6171<br>(-)</td><td>0.8015<br>(0.0061)</td><td>0.8186<br>(0.0066)</td><td>0.8015<br>(0.0061)</td><td>0.7951<br>(0.0066)</td></tr><tr><td>Significance (t = 0)</td><td>10,000<br>(-)</td><td>0.7998<br>(0.0070)</td><td>0.8170<br>(0.0079)</td><td>0.7998<br>(0.0070)</td><td>0.7933<br>(0.0078)</td></tr><tr><td>Significance (3205)</td><td>3205<br>(-)</td><td>0.7587<br>(0.0035)</td><td>0.7682<br>(0.0060)</td><td>0.7587<br>(0.0035)</td><td>0.7490<br>(0.0039)</td></tr><tr><td>Significance (4117)</td><td>4117<br>(-)</td><td>0.7952<br>(0.0052)</td><td>0.8076<br>(0.0040)</td><td>0.7952<br>(0.0052)</td><td>0.7860<br>(0.0053)</td></tr><tr><td>Significance (5566)</td><td>5566<br>(-)</td><td>0.8043<br>(0.0040)</td><td>0.8187<br>(0.0052)</td><td>0.8043<br>(0.0040)</td><td>0.7960<br>(0.0049)</td></tr><tr><td>Significance (6171)</td><td>6171<br>(-)</td><td>0.8121<br>(0.0042)</td><td>0.8258<br>(0.0037)</td><td>0.8121<br>(0.0042)</td><td>0.8047<br>(0.0042)</td></tr><tr><td>SpRST (4)</td><td>3205.333<br>(24.35296)</td><td>0.7733<br>(0.0058)</td><td>0.7878<br>(0.0062)</td><td>0.7733<br>(0.0058)</td><td>0.7652<br>(0.0059)</td></tr><tr><td>SpRST (5)</td><td>4117.000<br>(18.95257)</td><td>0.7823<br>(0.0065)</td><td>0.7980<br>(0.0072)</td><td>0.7823<br>(0.0065)</td><td>0.7743<br>(0.0070)</td></tr><tr><td>SpRST (8)</td><td>5566.000<br>(23.89142)</td><td>0.7931<br>(0.0060)</td><td>0.8092<br>(0.0053)</td><td>0.7931<br>(0.0060)</td><td>0.7861<br>(0.0063)</td></tr></tbody></table>
<table><caption>**Table 12** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>SpRST (10)</td><td>6171.333<br>(26.01282)</td><td>0.7938<br>(0.0063)</td><td>0.8107<br>(0.0063)</td><td>0.7938<br>(0.0063)</td><td>0.7870<br>(0.0069)</td></tr><tr><td>SumSquaresRatio (3205)</td><td>3205<br>(-)</td><td>0.8111<br>(0.0052)</td><td>0.8239<br>(0.0055)</td><td>0.8111<br>(0.0052)</td><td>0.8051<br>(0.0055)</td></tr><tr><td>SumSquaresRatio (4117)</td><td>4117<br>(-)</td><td>0.8091<br>(0.0035)</td><td>0.8212<br>(0.0037)</td><td>0.8091<br>(0.0035)</td><td>0.8034<br>(0.0038)</td></tr><tr><td>SumSquaresRatio (5566)</td><td>5566<br>(-)</td><td>0.8066<br>(0.0032)</td><td>0.8213<br>(0.0059)</td><td>0.8066<br>(0.0032)</td><td>0.8002<br>(0.0033)</td></tr><tr><td>SumSquaresRatio (6171)</td><td>6171<br>(-)</td><td>0.8045<br>(0.0044)</td><td>0.8203<br>(0.0048)</td><td>0.8045<br>(0.0044)</td><td>0.7982<br>(0.0048)</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>113<br>(-)</td><td>0.7117<br>(0.0044)</td><td>0.7182<br>(0.0038)</td><td>0.7117<br>(0.0044)</td><td>0.7079<br>(0.0044)</td></tr><tr><td>Symmetricaluncert (3205)</td><td>3205<br>(-)</td><td>0.7593<br>(0.0030)</td><td>0.7713<br>(0.0033)</td><td>0.7593<br>(0.0030)</td><td>0.7498<br>(0.0037)</td></tr><tr><td>Symmetricaluncert (4117)</td><td>4117<br>(-)</td><td>0.7930<br>(0.0042)</td><td>0.8055<br>(0.0054)</td><td>0.7930<br>(0.0042)</td><td>0.7839<br>(0.0047)</td></tr><tr><td>Symmetricaluncert (5566)</td><td>5566<br>(-)</td><td>0.8021<br>(0.0048)</td><td>0.8170<br>(0.0047)</td><td>0.8021<br>(0.0048)</td><td>0.7936<br>(0.0052)</td></tr><tr><td>Symmetricaluncert (6171)</td><td>6171<br>(-)</td><td>0.8089<br>(0.0042)</td><td>0.8242<br>(0.0054)</td><td>0.8089<br>(0.0042)</td><td>0.8011<br>(0.0048)</td></tr></tbody></table>

Averages and standard deviation in brackets
<table><caption>**Table 12** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>SpRST (10)</td><td>6171.333<br>(26.01282)</td><td>0.7938<br>(0.0063)</td><td>0.8107<br>(0.0063)</td><td>0.7938<br>(0.0063)</td><td>0.7870<br>(0.0069)</td></tr><tr><td>SumSquaresRatio (3205)</td><td>3205<br>(-)</td><td>0.8111<br>(0.0052)</td><td>0.8239<br>(0.0055)</td><td>0.8111<br>(0.0052)</td><td>0.8051<br>(0.0055)</td></tr><tr><td>SumSquaresRatio (4117)</td><td>4117<br>(-)</td><td>0.8091<br>(0.0035)</td><td>0.8212<br>(0.0037)</td><td>0.8091<br>(0.0035)</td><td>0.8034<br>(0.0038)</td></tr><tr><td>SumSquaresRatio (5566)</td><td>5566<br>(-)</td><td>0.8066<br>(0.0032)</td><td>0.8213<br>(0.0059)</td><td>0.8066<br>(0.0032)</td><td>0.8002<br>(0.0033)</td></tr><tr><td>SumSquaresRatio (6171)</td><td>6171<br>(-)</td><td>0.8045<br>(0.0044)</td><td>0.8203<br>(0.0048)</td><td>0.8045<br>(0.0044)</td><td>0.7982<br>(0.0048)</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>113<br>(-)</td><td>0.7117<br>(0.0044)</td><td>0.7182<br>(0.0038)</td><td>0.7117<br>(0.0044)</td><td>0.7079<br>(0.0044)</td></tr><tr><td>Symmetricaluncert (3205)</td><td>3205<br>(-)</td><td>0.7593<br>(0.0030)</td><td>0.7713<br>(0.0033)</td><td>0.7593<br>(0.0030)</td><td>0.7498<br>(0.0037)</td></tr><tr><td>Symmetricaluncert (4117)</td><td>4117<br>(-)</td><td>0.7930<br>(0.0042)</td><td>0.8055<br>(0.0054)</td><td>0.7930<br>(0.0042)</td><td>0.7839<br>(0.0047)</td></tr><tr><td>Symmetricaluncert (5566)</td><td>5566<br>(-)</td><td>0.8021<br>(0.0048)</td><td>0.8170<br>(0.0047)</td><td>0.8021<br>(0.0048)</td><td>0.7936<br>(0.0052)</td></tr><tr><td>Symmetricaluncert (6171)</td><td>6171<br>(-)</td><td>0.8089<br>(0.0042)</td><td>0.8242<br>(0.0054)</td><td>0.8089<br>(0.0042)</td><td>0.8011<br>(0.0048)</td></tr></tbody></table>

Averages and standard deviation in brackets
<table><caption>**Table 13** Summary of runtimes (in seconds)</caption><thead><tr><th>Method</th><th>#Features</th><th>Selection</th><th>Random forest</th><th>Naive Bayes</th></tr></thead><tbody><tr><td>Original</td><td>10,000<br>(-)</td><td>0<br>(-)</td><td>478.1466<br>(29.3932)</td><td>154.3220<br>(13.4778)</td></tr><tr><td>CV (t = 0)</td><td>4149<br>(-)</td><td>49<br>(-)</td><td>184.2093<br>(8.0784)</td><td>59.7090<br>(5.3565)</td></tr><tr><td>CV (3205)</td><td>3205<br>(-)</td><td>46<br>(-)</td><td>240.1020<br>(6.9240)</td><td>44.5060<br>(3.9978)</td></tr><tr><td>CV (4117)</td><td>4117<br>(-)</td><td>47<br>(-)</td><td>199.5176<br>(36.3792)</td><td>58.9020<br>(6.2692)</td></tr><tr><td>CV (5566)</td><td>5566<br>(-)</td><td>48<br>(-)</td><td>200.4283<br>(3.1720)</td><td>78.9990<br>(9.0758)</td></tr><tr><td>CV (6171)</td><td>6171<br>(-)</td><td>48<br>(-)</td><td>258.3198<br>(40.7532)</td><td>86.9790<br>(8.5235)</td></tr><tr><td>Cfs (Greedy)</td><td>41<br>(-)</td><td>296<br>(-)</td><td>72.0946<br>(7.3634)</td><td>1.1660<br>(0.1060)</td></tr><tr><td>Chi squared (t = 0)</td><td>113<br>(-)</td><td>2<br>(-)</td><td>91.0451<br>(6.9113)</td><td>2.0520<br>(0.2946)</td></tr><tr><td>Chi squared (3205)</td><td>3205<br>(-)</td><td>3<br>(-)</td><td>197.3493<br>(33.7688)</td><td>50.5860<br>(16.9048)</td></tr><tr><td>Chi squared (4117)</td><td>4117<br>(-)</td><td>2<br>(-)</td><td>258.7080<br>(21.4711)</td><td>59.6300<br>(5.5790)</td></tr><tr><td>Chi squared (5566)</td><td>5566<br>(-)</td><td>2<br>(-)</td><td>218.7398<br>(7.0253)</td><td>77.6480<br>(4.9175)</td></tr><tr><td>Chi squared (6171)</td><td>6171<br>(-)</td><td>3<br>(-)</td><td>233.1519<br>(13.3455)</td><td>84.5110<br>(4.3186)</td></tr><tr><td>Consistency (Greedy)</td><td>30<br>(-)</td><td>608<br>(-)</td><td>55.0971<br>(5.3676)</td><td>0.9240<br>(0.0534)</td></tr><tr><td>Correlation (t = 0)</td><td>10,000<br>(-)</td><td>10<br>(-)</td><td>339.7830<br>(20.4477)</td><td>135.5330<br>(6.5574)</td></tr><tr><td>Correlation (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>222.5547<br>(8.3334)</td><td>39.6600<br>(2.3442)</td></tr><tr><td>Correlation (4117)</td><td>4117<br>(-)</td><td>8<br>(-)</td><td>409.2935<br>(266.9731)</td><td>53.0690<br>(2.9146)</td></tr><tr><td>Correlation (5566)</td><td>5566<br>(-)</td><td>9<br>(-)</td><td>242.3133<br>(13.5045)</td><td>74.3500<br>(4.3644)</td></tr><tr><td>Correlation (6171)</td><td>6171<br>(-)</td><td>8<br>(-)</td><td>277.2288<br>(9.0772)</td><td>81.8020<br>(4.5509)</td></tr><tr><td>Gain ratio (t = 0)</td><td>113<br>(-)</td><td>7<br>(-)</td><td>88.3953<br>(7.3338)</td><td>1.9850<br>(0.1276)</td></tr><tr><td>Gain ratio (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>211.9993<br>(8.9143)</td><td>44.6560<br>(1.6366)</td></tr><tr><td>Gain ratio (4117)</td><td>4117<br>(-)</td><td>9<br>(-)</td><td>207.8572<br>(33.3359)</td><td>58.0990<br>(2.7974)</td></tr></tbody></table>
<table><caption>**Table 13** Summary of runtimes (in seconds)</caption><thead><tr><th>Method</th><th>#Features</th><th>Selection</th><th>Random forest</th><th>Naive Bayes</th></tr></thead><tbody><tr><td>Original</td><td>10,000<br>(-)</td><td>0<br>(-)</td><td>478.1466<br>(29.3932)</td><td>154.3220<br>(13.4778)</td></tr><tr><td>CV (t = 0)</td><td>4149<br>(-)</td><td>49<br>(-)</td><td>184.2093<br>(8.0784)</td><td>59.7090<br>(5.3565)</td></tr><tr><td>CV (3205)</td><td>3205<br>(-)</td><td>46<br>(-)</td><td>240.1020<br>(6.9240)</td><td>44.5060<br>(3.9978)</td></tr><tr><td>CV (4117)</td><td>4117<br>(-)</td><td>47<br>(-)</td><td>199.5176<br>(36.3792)</td><td>58.9020<br>(6.2692)</td></tr><tr><td>CV (5566)</td><td>5566<br>(-)</td><td>48<br>(-)</td><td>200.4283<br>(3.1720)</td><td>78.9990<br>(9.0758)</td></tr><tr><td>CV (6171)</td><td>6171<br>(-)</td><td>48<br>(-)</td><td>258.3198<br>(40.7532)</td><td>86.9790<br>(8.5235)</td></tr><tr><td>Cfs (Greedy)</td><td>41<br>(-)</td><td>296<br>(-)</td><td>72.0946<br>(7.3634)</td><td>1.1660<br>(0.1060)</td></tr><tr><td>Chi squared (t = 0)</td><td>113<br>(-)</td><td>2<br>(-)</td><td>91.0451<br>(6.9113)</td><td>2.0520<br>(0.2946)</td></tr><tr><td>Chi squared (3205)</td><td>3205<br>(-)</td><td>3<br>(-)</td><td>197.3493<br>(33.7688)</td><td>50.5860<br>(16.9048)</td></tr><tr><td>Chi squared (4117)</td><td>4117<br>(-)</td><td>2<br>(-)</td><td>258.7080<br>(21.4711)</td><td>59.6300<br>(5.5790)</td></tr><tr><td>Chi squared (5566)</td><td>5566<br>(-)</td><td>2<br>(-)</td><td>218.7398<br>(7.0253)</td><td>77.6480<br>(4.9175)</td></tr><tr><td>Chi squared (6171)</td><td>6171<br>(-)</td><td>3<br>(-)</td><td>233.1519<br>(13.3455)</td><td>84.5110<br>(4.3186)</td></tr><tr><td>Consistency (Greedy)</td><td>30<br>(-)</td><td>608<br>(-)</td><td>55.0971<br>(5.3676)</td><td>0.9240<br>(0.0534)</td></tr><tr><td>Correlation (t = 0)</td><td>10,000<br>(-)</td><td>10<br>(-)</td><td>339.7830<br>(20.4477)</td><td>135.5330<br>(6.5574)</td></tr><tr><td>Correlation (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>222.5547<br>(8.3334)</td><td>39.6600<br>(2.3442)</td></tr><tr><td>Correlation (4117)</td><td>4117<br>(-)</td><td>8<br>(-)</td><td>409.2935<br>(266.9731)</td><td>53.0690<br>(2.9146)</td></tr><tr><td>Correlation (5566)</td><td>5566<br>(-)</td><td>9<br>(-)</td><td>242.3133<br>(13.5045)</td><td>74.3500<br>(4.3644)</td></tr><tr><td>Correlation (6171)</td><td>6171<br>(-)</td><td>8<br>(-)</td><td>277.2288<br>(9.0772)</td><td>81.8020<br>(4.5509)</td></tr><tr><td>Gain ratio (t = 0)</td><td>113<br>(-)</td><td>7<br>(-)</td><td>88.3953<br>(7.3338)</td><td>1.9850<br>(0.1276)</td></tr><tr><td>Gain ratio (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>211.9993<br>(8.9143)</td><td>44.6560<br>(1.6366)</td></tr><tr><td>Gain ratio (4117)</td><td>4117<br>(-)</td><td>9<br>(-)</td><td>207.8572<br>(33.3359)</td><td>58.0990<br>(2.7974)</td></tr></tbody></table>
<table><caption>**Table 13** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Selection</th><th>Random forest</th><th>Naive Bayes</th></tr></thead><tbody><tr><td>Gain ratio (5566)</td><td>5566<br>(-)</td><td>8<br>(-)</td><td>229.9701<br>(10.6783)</td><td>77.2070<br>(3.2169)</td></tr><tr><td>Gain ratio (6171)</td><td>6171<br>(-)</td><td>9<br>(-)</td><td>236.6279<br>(10.8751)</td><td>88.3710<br>(7.0199)</td></tr><tr><td>Info gain (t = 0)</td><td>113<br>(-)</td><td>7<br>(-)</td><td>81.1204<br>(2.7119)</td><td>2.0050<br>(0.1910)</td></tr><tr><td>Info gain (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>203.8654<br>(5.2012)</td><td>45.8720<br>(3.2261)</td></tr><tr><td>Info gain (4117)</td><td>4117<br>(-)</td><td>8<br>(-)</td><td>234.0230<br>(31.3471)</td><td>58.9890<br>(2.7353)</td></tr><tr><td>Info gain (5566)</td><td>5566<br>(-)</td><td>9<br>(-)</td><td>200.8629<br>(17.7479)</td><td>78.1260<br>(3.4878)</td></tr><tr><td>Info gain (6171)</td><td>6171<br>(-)</td><td>8<br>(-)</td><td>227.2942<br>(3.2503)</td><td>85.6750<br>(5.4645)</td></tr><tr><td>ReliefF (t = 0)</td><td>7957<br>(-)</td><td>1<br>(-)</td><td>327.6763<br>(63.6214)</td><td>106.4700<br>(6.8651)</td></tr><tr><td>ReliefF (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>199.1945<br>(6.3894)</td><td>41.4520<br>(2.0377)</td></tr><tr><td>ReliefF (4117)</td><td>4117<br>(-)</td><td>8<br>(-)</td><td>225.2873<br>(6.5349)</td><td>53.7170<br>(3.5077)</td></tr><tr><td>ReliefF (5566)</td><td>5566<br>(-)</td><td>9<br>(-)</td><td>253.8634<br>(8.2094)</td><td>73.7800<br>(5.1618)</td></tr><tr><td>ReliefF (6171)</td><td>6171<br>(-)</td><td>9<br>(-)</td><td>321.8576<br>(87.1975)</td><td>80.0170<br>(3.2470)</td></tr><tr><td>Significance (t = 0)</td><td>10,000<br>(-)</td><td>3<br>(-)</td><td>345.5262<br>(47.0985)</td><td>135.7860<br>(5.4555)</td></tr><tr><td>Significance (3205)</td><td>3205<br>(-)</td><td>3<br>(-)</td><td>150.2230<br>(4.9146)</td><td>45.2610<br>(2.3983)</td></tr><tr><td>Significance (4117)</td><td>4117<br>(-)</td><td>2<br>(-)</td><td>161.6598<br>(8.0198)</td><td>58.9500<br>(3.0718)</td></tr><tr><td>Significance (5566)</td><td>5566<br>(-)</td><td>3<br>(-)</td><td>326.4685<br>(14.4817)</td><td>76.0120<br>(3.0610)</td></tr><tr><td>Significance (6171)</td><td>6171<br>(-)</td><td>3<br>(-)</td><td>227.0810<br>(43.6380)</td><td>84.6230<br>(4.6004)</td></tr><tr><td>SpRST (4)</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>3205.333<br>(24.35296)</td><td>1 node: 51.98853<br>2 nodes: 51<br>4 nodes: 25.86191<br>8 nodes: 20.81937<br>16 nodes: 18.0587<br>32 nodes: 16.60878</td><td>239.5910<br>(97.0897)</td><td>39.8933<br>(1.2833)</td></tr></tbody></table>
<table><caption>**Table 13** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Selection</th><th>Random forest</th><th>Naive Bayes</th></tr></thead><tbody><tr><td>Gain ratio (5566)</td><td>5566<br>(-)</td><td>8<br>(-)</td><td>229.9701<br>(10.6783)</td><td>77.2070<br>(3.2169)</td></tr><tr><td>Gain ratio (6171)</td><td>6171<br>(-)</td><td>9<br>(-)</td><td>236.6279<br>(10.8751)</td><td>88.3710<br>(7.0199)</td></tr><tr><td>Info gain (t = 0)</td><td>113<br>(-)</td><td>7<br>(-)</td><td>81.1204<br>(2.7119)</td><td>2.0050<br>(0.1910)</td></tr><tr><td>Info gain (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>203.8654<br>(5.2012)</td><td>45.8720<br>(3.2261)</td></tr><tr><td>Info gain (4117)</td><td>4117<br>(-)</td><td>8<br>(-)</td><td>234.0230<br>(31.3471)</td><td>58.9890<br>(2.7353)</td></tr><tr><td>Info gain (5566)</td><td>5566<br>(-)</td><td>9<br>(-)</td><td>200.8629<br>(17.7479)</td><td>78.1260<br>(3.4878)</td></tr><tr><td>Info gain (6171)</td><td>6171<br>(-)</td><td>8<br>(-)</td><td>227.2942<br>(3.2503)</td><td>85.6750<br>(5.4645)</td></tr><tr><td>ReliefF (t = 0)</td><td>7957<br>(-)</td><td>1<br>(-)</td><td>327.6763<br>(63.6214)</td><td>106.4700<br>(6.8651)</td></tr><tr><td>ReliefF (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>199.1945<br>(6.3894)</td><td>41.4520<br>(2.0377)</td></tr><tr><td>ReliefF (4117)</td><td>4117<br>(-)</td><td>8<br>(-)</td><td>225.2873<br>(6.5349)</td><td>53.7170<br>(3.5077)</td></tr><tr><td>ReliefF (5566)</td><td>5566<br>(-)</td><td>9<br>(-)</td><td>253.8634<br>(8.2094)</td><td>73.7800<br>(5.1618)</td></tr><tr><td>ReliefF (6171)</td><td>6171<br>(-)</td><td>9<br>(-)</td><td>321.8576<br>(87.1975)</td><td>80.0170<br>(3.2470)</td></tr><tr><td>Significance (t = 0)</td><td>10,000<br>(-)</td><td>3<br>(-)</td><td>345.5262<br>(47.0985)</td><td>135.7860<br>(5.4555)</td></tr><tr><td>Significance (3205)</td><td>3205<br>(-)</td><td>3<br>(-)</td><td>150.2230<br>(4.9146)</td><td>45.2610<br>(2.3983)</td></tr><tr><td>Significance (4117)</td><td>4117<br>(-)</td><td>2<br>(-)</td><td>161.6598<br>(8.0198)</td><td>58.9500<br>(3.0718)</td></tr><tr><td>Significance (5566)</td><td>5566<br>(-)</td><td>3<br>(-)</td><td>326.4685<br>(14.4817)</td><td>76.0120<br>(3.0610)</td></tr><tr><td>Significance (6171)</td><td>6171<br>(-)</td><td>3<br>(-)</td><td>227.0810<br>(43.6380)</td><td>84.6230<br>(4.6004)</td></tr><tr><td>SpRST (4)</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>3205.333<br>(24.35296)</td><td>1 node: 51.98853<br>2 nodes: 51<br>4 nodes: 25.86191<br>8 nodes: 20.81937<br>16 nodes: 18.0587<br>32 nodes: 16.60878</td><td>239.5910<br>(97.0897)</td><td>39.8933<br>(1.2833)</td></tr></tbody></table>
<table><caption>**Table 13** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Selection</th><th>Random forest</th><th>Naive Bayes</th></tr></thead><tbody><tr><td rowspan="3">SpRST (5)</td><td></td><td>1 node: 72.27206</td><td></td><td></td></tr><tr><td></td><td>2 nodes: 52.9</td><td></td><td></td></tr><tr><td>4117.000<br>(18.95257)</td><td>4 nodes: 49.97752<br>8 nodes: 28.46254<br>16 nodes: 22.83241<br>32 nodes: 18.30543</td><td>227.8856<br>(49.6911)</td><td>51.9187<br>(2.8680)</td></tr><tr><td rowspan="3">SpRST (8)</td><td></td><td>1 node: 935.9299</td><td></td><td></td></tr><tr><td></td><td>2 nodes: 374.9</td><td></td><td></td></tr><tr><td>5566.000<br>(23.89142)</td><td>4 nodes: 646.0979<br>8 nodes: 209.8693<br>16 nodes: 118.5509<br>32 nodes: 88.09231</td><td>257.9724<br>(53.8414)</td><td>72.9547<br>(5.2135)</td></tr><tr><td rowspan="3">SpRST (10)</td><td></td><td>1 node: 4448.072</td><td></td><td></td></tr><tr><td></td><td>2 nodes: 1540.4</td><td></td><td></td></tr><tr><td>6171.333<br>(26.01282)</td><td>4 nodes: 1641.565<br>8 nodes: 865.2048<br>16 nodes: 585.8696<br>32 nodes: 334.1958</td><td>245.9802<br>(46.8001)</td><td>79.3787<br>(3.6052)</td></tr><tr><td>Sum squares ratio (3205)</td><td>3205<br>(-)</td><td>2<br>(-)</td><td>201.7381<br>(7.0755)</td><td>41.4670<br>(1.7756)</td></tr><tr><td>Sum squares ratio (4117)</td><td>4117<br>(-)</td><td>2<br>(-)</td><td>231.8041<br>(21.1133)</td><td>58.8280<br>(11.8832)</td></tr><tr><td>Sum squares ratio (5566)</td><td>5566<br>(-)</td><td>2<br>(-)</td><td>246.9675<br>(10.4719)</td><td>77.6570<br>(10.3124)</td></tr><tr><td>Sum squares ratio (6171)</td><td>6171<br>(-)</td><td>2<br>(-)</td><td>363.0283<br>(65.0364)</td><td>84.5040<br>(9.1140)</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>113<br>(-)</td><td>7<br>(-)</td><td>90.2343<br>(7.6894)</td><td>2.0700<br>(0.4490)</td></tr><tr><td>Symmetricaluncert (3205)</td><td>3205<br>(-)</td><td>8<br>(-)</td><td>163.9254<br>(47.5122)</td><td>45.9670<br>(3.1956)</td></tr><tr><td>Symmetricaluncert (4117)</td><td>4117<br>(-)</td><td>9<br>(-)</td><td>158.4968<br>(5.7051)</td><td>59.0650<br>(4.4659)</td></tr><tr><td>Symmetricaluncert (5566)</td><td>5566<br>(-)</td><td>8<br>(-)</td><td>249.9402<br>(64.8124)</td><td>76.5380<br>(5.3429)</td></tr><tr><td>Symmetricaluncert (6171)</td><td>6171<br>(-)</td><td>9<br>(-)</td><td>282.1294<br>(74.9137)</td><td>917.6640<br>(2630.7532)</td></tr></tbody></table>

Averages and standard deviation in brackets
<table><caption>**Table 13** continued</caption><thead><tr><th>Method</th><th>#Features</th><th>Selection</th><th>Random forest</th><th>Naive Bayes</th></tr></thead><tbody><tr><td rowspan="3">SpRST (5)</td><td rowspan="3">4117.000<br>(18.95257)</td><td>1 node: 72.27206</td><td></td><td></td></tr><tr><td>2 nodes: 52.9</td><td></td><td></td></tr><tr><td>4 nodes: 49.97752</td><td>227.8856</td><td>51.9187</td></tr><tr><td rowspan="5">SpRST (8)</td><td rowspan="5">5566.000<br>(23.89142)</td><td>8 nodes: 28.46254</td><td>(49.6911)</td><td>(2.8680)</td></tr><tr><td>16 nodes: 22.83241</td><td></td><td></td></tr><tr><td>32 nodes: 18.30543</td><td></td><td></td></tr><tr><td>1 node: 935.9299</td><td></td><td></td></tr><tr><td>2 nodes: 374.9</td><td></td><td></td></tr><tr><td rowspan="5">SpRST (10)</td><td rowspan="5">6171.333<br>(26.01282)</td><td>4 nodes: 646.0979</td><td>257.9724</td><td>72.9547</td></tr><tr><td>8 nodes: 209.8693</td><td>(53.8414)</td><td>(5.2135)</td></tr><tr><td>16 nodes: 118.5509</td><td></td><td></td></tr><tr><td>32 nodes: 88.09231</td><td></td><td></td></tr><tr><td>1 node: 4448.072</td><td></td><td></td></tr><tr><td rowspan="5">Sum squares ratio (3205)</td><td rowspan="5">3205<br>(-)</td><td>2</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td>8 nodes: 865.2048</td><td>(46.8001)</td><td>(3.6052)</td></tr><tr><td>16 nodes: 585.8696</td><td></td><td></td></tr><tr><td>32 nodes: 334.1958</td><td></td><td></td></tr><tr><td rowspan="2">Sum squares ratio (4117)</td><td rowspan="2">4117<br>(-)</td><td>2</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Sum squares ratio (5566)</td><td rowspan="2">5566<br>(-)</td><td>2</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Sum squares ratio (6171)</td><td rowspan="2">6171<br>(-)</td><td>2</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Symmetricaluncert (t = 0)</td><td rowspan="2">113<br>(-)</td><td>7</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Symmetricaluncert (3205)</td><td rowspan="2">3205<br>(-)</td><td>8</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Symmetricaluncert (4117)</td><td rowspan="2">4117<br>(-)</td><td>9</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Symmetricaluncert (5566)</td><td rowspan="2">5566<br>(-)</td><td>8</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr><tr><td rowspan="2">Symmetricaluncert (6171)</td><td rowspan="2">6171<br>(-)</td><td>9</td><td></td><td></td></tr><tr><td>4 nodes: 1641.565</td><td>245.9802</td><td>79.3787</td></tr></tbody></table>

Averages and standard deviation in brackets
# Appendix B: Results of Wilcoxon rank sum tests

See Tables 14, 15, 16, 17, 18, 19, 20 and 21

<table><caption>**Table 14** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the accuracy metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>1.1e-09</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt; 2e-16</td><td>6.0e-15</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt; 2e-16</td><td>&lt; 2e-16</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0170</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0032</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr><tr><td>Consistency (Greedy)</td><td>0.0026</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0022</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0019</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0026</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0176</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0031</td><td>0.0139</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0061</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0157</td></tr></tbody></table>
# Appendix B: Results of Wilcoxon rank sum tests

See Tables 14, 15, 16, 17, 18, 19, 20 and 21

<table><caption>**Table 14** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the accuracy metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>1.1e-09</td><td>–</td><td>–</td><td>–</td></tr><tr><td>SpRST (8)</td><td>&lt; 2e-16</td><td>6.0e-15</td><td>–</td><td>–</td></tr><tr><td>SpRST (10)</td><td>&lt; 2e-16</td><td>&lt; 2e-16</td><td>1.0000</td><td>–</td></tr><tr><td>Original (–)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0170</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0032</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr><tr><td>Consistency (Greedy)</td><td>0.0026</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0022</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0019</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0026</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0176</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0031</td><td>0.0139</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0061</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0157</td></tr></tbody></table>
<table><caption>**Table 14** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr></tbody></table>

<table><caption>**Table 15** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the recall metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>1.7e-09</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt;2e-16</td><td>5.9e-15</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>&lt;2e-16</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0181</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0035</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr><tr><td>Consistency (Greedy)</td><td>0.0025</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0022</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0019</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0025</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0178</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 14** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>1.0000</td><td>0.0177</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0051</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0041</td><td>1.0000</td><td>0.0403</td><td>0.0038</td></tr></tbody></table>

<table><caption>**Table 15** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the recall metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>1.7e-09</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt;2e-16</td><td>5.9e-15</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>&lt;2e-16</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0181</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0035</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr><tr><td>Consistency (Greedy)</td><td>0.0025</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0022</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0019</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0025</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0178</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 15** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0167</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0060</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0154</td></tr><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr></tbody></table>

<table><caption>**Table 16** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the precision metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>3.3e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt; 2e-16</td><td>2.2e-13</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt; 2e-16</td><td>1.1e-14</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0540</td><td>0.1821</td></tr><tr><td>Cfs (Greedy)</td><td>0.6941</td><td>1.0000</td><td>0.0060</td><td>0.0031</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr></tbody></table>
<table><caption>**Table 15** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0167</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0060</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0032</td><td>0.0154</td></tr><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>1.0000</td><td>0.0204</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0044</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0042</td><td>1.0000</td><td>0.0383</td><td>0.0038</td></tr></tbody></table>

<table><caption>**Table 16** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the precision metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>3.3e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt; 2e-16</td><td>2.2e-13</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt; 2e-16</td><td>1.1e-14</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0540</td><td>0.1821</td></tr><tr><td>Cfs (Greedy)</td><td>0.6941</td><td>1.0000</td><td>0.0060</td><td>0.0031</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr></tbody></table>
<table><caption>**Table 16** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0132</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0019</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0044</td><td>0.0065</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0019</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0024</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0715</td><td>0.0029</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>1.0000</td><td>0.0041</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0132</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0138</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0040</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0040</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0167</td><td>0.0325</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0142</td><td>0.0247</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0126</td><td>0.0246</td></tr><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0132</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0060</td><td>0.0101</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0138</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 16** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0132</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0019</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0044</td><td>0.0065</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0020</td><td>0.0019</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0024</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0715</td><td>0.0029</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>1.0000</td><td>0.0041</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0132</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0138</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0040</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0040</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0167</td><td>0.0325</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0142</td><td>0.0247</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0126</td><td>0.0246</td></tr><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0132</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0060</td><td>0.0101</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0122</td><td>1.0000</td><td>0.0787</td><td>0.0194</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0138</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0040</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 17** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the F1 metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>6e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt;2e-16</td><td>1e-14</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>&lt;2e-16</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0074</td><td>0.0562</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0270</td><td>1.0000</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0021</td><td>0.0033</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0019</td><td>0.0022</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0024</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0399</td><td>0.0019</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0018</td><td>0.8417</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0058</td><td>0.0326</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0033</td><td>0.0094</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0058</td><td>0.0230</td></tr><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0023</td><td>0.0033</td></tr></tbody></table>
<table><caption>**Table 17** Results of Wilcoxon rank sum tests with Bonferroni correction for Naive Bayes and the F1 metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (5)</td><td>6e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (8)</td><td>&lt;2e-16</td><td>1e-14</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>&lt;2e-16</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.0074</td><td>0.0562</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0270</td><td>1.0000</td></tr><tr><td>Chi squared (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0021</td><td>0.0033</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0019</td><td>0.0022</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0024</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0399</td><td>0.0019</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0018</td><td>0.0018</td><td>0.8417</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0018</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0058</td><td>0.0326</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0033</td><td>0.0094</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0058</td><td>0.0230</td></tr><tr><td>Significance (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0023</td><td>0.0033</td></tr></tbody></table>
<table><caption>**Table 17** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>

<table><caption>**Table 18** Results of Wilcoxon rank sum tests with Bonferroni correction for random forest and the accuracy metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>2.2e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt; 2e-16</td><td>2.5e-09</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt; 2e-16</td><td>4.1e-10</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.1252</td><td>0.5138</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (3205)</td><td>0.0061</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.0020</td><td>0.0897</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0024</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0113</td><td>0.0216</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0022</td><td>0.0616</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0058</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0024</td><td>0.4050</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0020</td><td>0.2470</td><td>0.7423</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0031</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0100</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.0024</td><td>0.1245</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0022</td><td>0.3406</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0361</td><td>0.0803</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 17** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.5275</td><td>0.0032</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.1024</td><td>1.0000</td><td>0.5425</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0019</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>1.0000</td><td>1.0000</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>

<table><caption>**Table 18** Results of Wilcoxon rank sum tests with Bonferroni correction for random forest and the accuracy metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>2.2e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt; 2e-16</td><td>2.5e-09</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt; 2e-16</td><td>4.1e-10</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.1252</td><td>0.5138</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (3205)</td><td>0.0061</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.0020</td><td>0.0897</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0024</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0113</td><td>0.0216</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0022</td><td>0.0616</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0058</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0024</td><td>0.4050</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0020</td><td>0.2470</td><td>0.7423</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0031</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0100</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.0024</td><td>0.1245</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0022</td><td>0.3406</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0361</td><td>0.0803</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 18** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Info gain (3205)</td><td>0.0453</td><td>0.0020</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.0022</td><td>0.1735</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0022</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Info gain (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0020</td><td>0.0285</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0028</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0035</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0170</td><td>0.0359</td></tr><tr><td>Significance (3205)</td><td>0.0030</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.0022</td><td>0.0509</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0333</td><td>0.0831</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0056</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0022</td><td>0.0028</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0028</td><td>0.0051</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0263</td><td>0.0640</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0035</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0024</td><td>0.1251</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0019</td><td>0.6292</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0032</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>

<table><caption>**Table 19** Results of Wilcoxon rank sum tests with Bonferroni correction for Random forest and the recall metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>2.2e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt;2e-16</td><td>2.5e-09</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>4.1e-10</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.1252</td><td>0.5138</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (3205)</td><td>0.0061</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.0020</td><td>0.0897</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0024</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0113</td><td>0.0216</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 18** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Info gain (3205)</td><td>0.0453</td><td>0.0020</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.0022</td><td>0.1735</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0022</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Info gain (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0020</td><td>0.0285</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0028</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0035</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0170</td><td>0.0359</td></tr><tr><td>Significance (3205)</td><td>0.0030</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.0022</td><td>0.0509</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0333</td><td>0.0831</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0056</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0022</td><td>0.0028</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0028</td><td>0.0051</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0263</td><td>0.0640</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0035</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0024</td><td>0.1251</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0019</td><td>0.6292</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0032</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>

<table><caption>**Table 19** Results of Wilcoxon rank sum tests with Bonferroni correction for Random forest and the recall metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>2.2e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt;2e-16</td><td>2.5e-09</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>4.1e-10</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0018</td><td>0.0018</td><td>0.1252</td><td>0.5138</td></tr><tr><td>Cfs (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (3205)</td><td>0.0061</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Chi squared (4117)</td><td>0.0020</td><td>0.0897</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0018</td><td>0.0024</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0113</td><td>0.0216</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 19** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0022</td><td>0.0616</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0058</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0024</td><td>0.4050</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0020</td><td>0.2470</td><td>0.7423</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0031</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0100</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.0024</td><td>0.1245</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0022</td><td>0.3406</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0361</td><td>0.0803</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (3205)</td><td>0.0453</td><td>0.0020</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.0022</td><td>0.1735</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0022</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Info gain (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0020</td><td>0.0285</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0028</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0035</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0170</td><td>0.0359</td></tr><tr><td>Significance (3205)</td><td>0.0030</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.0022</td><td>0.0509</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0333</td><td>0.0831</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0056</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0022</td><td>0.0028</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0028</td><td>0.0051</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0263</td><td>0.0640</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0035</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0024</td><td>0.1251</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0019</td><td>0.6292</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0032</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 19** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Consistency (Greedy)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Correlation (3205)</td><td>0.0022</td><td>0.0616</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0018</td><td>0.0058</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0018</td><td>0.0024</td><td>0.4050</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0018</td><td>0.0020</td><td>0.2470</td><td>0.7423</td></tr><tr><td>Correlation (t = 0)</td><td>0.0018</td><td>0.0031</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>CV (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (3205)</td><td>0.0100</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Gain ratio (4117)</td><td>0.0024</td><td>0.1245</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0018</td><td>0.0022</td><td>0.3406</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0361</td><td>0.0803</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (3205)</td><td>0.0453</td><td>0.0020</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Info gain (4117)</td><td>0.0022</td><td>0.1735</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0018</td><td>0.0022</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Info gain (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>ReliefF (3205)</td><td>0.0020</td><td>0.0285</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0018</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0018</td><td>0.0028</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0018</td><td>0.0035</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0170</td><td>0.0359</td></tr><tr><td>Significance (3205)</td><td>0.0030</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Significance (4117)</td><td>0.0022</td><td>0.0509</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0333</td><td>0.0831</td></tr><tr><td>Significance (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0020</td></tr><tr><td>Significance (t = 0)</td><td>0.0018</td><td>0.0056</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0018</td><td>0.0018</td><td>0.0024</td><td>0.0032</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0018</td><td>0.0018</td><td>0.0022</td><td>0.0028</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0018</td><td>0.0018</td><td>0.0028</td><td>0.0051</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0018</td><td>0.0020</td><td>0.0263</td><td>0.0640</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0035</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0024</td><td>0.1251</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0018</td><td>0.0019</td><td>0.6292</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0032</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0018</td><td>0.0018</td><td>0.0018</td><td>0.0018</td></tr></tbody></table>
<table><caption>**Table 20** Results of Wilcoxon rank sum tests with Bonferroni correction for random forest and the precision metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>1.1e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt;2e-16</td><td>1.1e-09</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>8.5e-11</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0019</td><td>0.0034</td><td>0.3327</td><td>1.0000</td></tr><tr><td>Cfs (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (3205)</td><td>0.0031</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (4117)</td><td>0.0029</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0019</td><td>0.0110</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0019</td><td>0.0024</td><td>0.0289</td><td>0.1315</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Consistency (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Correlation (3205)</td><td>0.0024</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0019</td><td>0.0094</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0019</td><td>0.0034</td><td>0.7525</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0019</td><td>0.0029</td><td>0.0783</td><td>1.0000</td></tr><tr><td>Correlation (t = 0)</td><td>0.0019</td><td>0.0052</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (4117)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (5566)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (6171)</td><td>0.0079</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Gain ratio (3205)</td><td>0.0034</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Gain ratio (4117)</td><td>0.0024</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0019</td><td>0.0102</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0019</td><td>0.0031</td><td>0.1415</td><td>0.4696</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (3205)</td><td>0.0067</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (4117)</td><td>0.0024</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0019</td><td>0.0067</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0247</td></tr><tr><td>Info gain (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>ReliefF (3205)</td><td>0.0022</td><td>0.2696</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0019</td><td>0.0120</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0019</td><td>0.0086</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0019</td><td>0.0057</td><td>0.5027</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0019</td><td>0.0022</td><td>0.0073</td><td>0.1223</td></tr><tr><td>Significance (3205)</td><td>0.0022</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Significance (4117)</td><td>0.0022</td><td>0.2696</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0019</td><td>0.0031</td><td>0.1636</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Significance (t = 0)</td><td>0.0019</td><td>0.0153</td><td>1.0000</td><td>1.0000</td></tr></tbody></table>
<table><caption>**Table 20** Results of Wilcoxon rank sum tests with Bonferroni correction for random forest and the precision metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>1.1e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt;2e-16</td><td>1.1e-09</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>8.5e-11</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0019</td><td>0.0034</td><td>0.3327</td><td>1.0000</td></tr><tr><td>Cfs (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (3205)</td><td>0.0031</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (4117)</td><td>0.0029</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0019</td><td>0.0110</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0019</td><td>0.0024</td><td>0.0289</td><td>0.1315</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Consistency (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Correlation (3205)</td><td>0.0024</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0019</td><td>0.0094</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0019</td><td>0.0034</td><td>0.7525</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0019</td><td>0.0029</td><td>0.0783</td><td>1.0000</td></tr><tr><td>Correlation (t = 0)</td><td>0.0019</td><td>0.0052</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (4117)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (5566)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (6171)</td><td>0.0079</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Gain ratio (3205)</td><td>0.0034</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Gain ratio (4117)</td><td>0.0024</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0019</td><td>0.0102</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0019</td><td>0.0031</td><td>0.1415</td><td>0.4696</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (3205)</td><td>0.0067</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (4117)</td><td>0.0024</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0019</td><td>0.0067</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0247</td></tr><tr><td>Info gain (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>ReliefF (3205)</td><td>0.0022</td><td>0.2696</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0019</td><td>0.0120</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0019</td><td>0.0086</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0019</td><td>0.0057</td><td>0.5027</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0019</td><td>0.0022</td><td>0.0073</td><td>0.1223</td></tr><tr><td>Significance (3205)</td><td>0.0022</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Significance (4117)</td><td>0.0022</td><td>0.2696</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0019</td><td>0.0031</td><td>0.1636</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Significance (t = 0)</td><td>0.0019</td><td>0.0153</td><td>1.0000</td><td>1.0000</td></tr></tbody></table>
<table><caption>**Table 20** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Sum squares ratio (3205)</td><td>0.0019</td><td>0.0020</td><td>0.0062</td><td>0.0210</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0019</td><td>0.0022</td><td>0.0067</td><td>0.0727</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0019</td><td>0.0029</td><td>0.0338</td><td>0.6158</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0019</td><td>0.0026</td><td>0.0267</td><td>0.1759</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0020</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0073</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0019</td><td>0.0041</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0019</td><td>0.0022</td><td>0.0024</td><td>0.0130</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr></tbody></table>

<table><caption>**Table 21** Results of Wilcoxon rank sum tests with Bonferroni correction for random forest and the F1 metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>9.7e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt;2e-16</td><td>9.0e-10</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>2.6e-10</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0019</td><td>0.0019</td><td>0.1415</td><td>0.9173</td></tr><tr><td>Cfs (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (3205)</td><td>0.0048</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (4117)</td><td>0.0031</td><td>0.8589</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0019</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0228</td><td>0.0980</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Consistency (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Correlation (3205)</td><td>0.0022</td><td>0.0460</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0019</td><td>0.0052</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0019</td><td>0.0024</td><td>0.4095</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0019</td><td>0.0019</td><td>0.2513</td><td>1.0000</td></tr><tr><td>Correlation (t = 0)</td><td>0.0019</td><td>0.0031</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (4117)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (5566)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr></tbody></table>
<table><caption>**Table 20** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Sum squares ratio (3205)</td><td>0.0019</td><td>0.0020</td><td>0.0062</td><td>0.0210</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0019</td><td>0.0022</td><td>0.0067</td><td>0.0727</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0019</td><td>0.0029</td><td>0.0338</td><td>0.6158</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0019</td><td>0.0026</td><td>0.0267</td><td>0.1759</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0020</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0073</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0019</td><td>0.0041</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0019</td><td>0.0022</td><td>0.0024</td><td>0.0130</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr></tbody></table>

<table><caption>**Table 21** Results of Wilcoxon rank sum tests with Bonferroni correction for random forest and the F1 metric</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>SpRST (05)</td><td>9.7e-07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SpRST (08)</td><td>&lt;2e-16</td><td>9.0e-10</td><td>-</td><td>-</td></tr><tr><td>SpRST (10)</td><td>&lt;2e-16</td><td>2.6e-10</td><td>1.0000</td><td>-</td></tr><tr><td>Original (-)</td><td>0.0019</td><td>0.0019</td><td>0.1415</td><td>0.9173</td></tr><tr><td>Cfs (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (3205)</td><td>0.0048</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Chi squared (4117)</td><td>0.0031</td><td>0.8589</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (5566)</td><td>0.0019</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Chi squared (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0228</td><td>0.0980</td></tr><tr><td>Chi squared (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Consistency (Greedy)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Correlation (3205)</td><td>0.0022</td><td>0.0460</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (4117)</td><td>0.0019</td><td>0.0052</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Correlation (5566)</td><td>0.0019</td><td>0.0024</td><td>0.4095</td><td>1.0000</td></tr><tr><td>Correlation (6171)</td><td>0.0019</td><td>0.0019</td><td>0.2513</td><td>1.0000</td></tr><tr><td>Correlation (t = 0)</td><td>0.0019</td><td>0.0031</td><td>1.0000</td><td>1.0000</td></tr><tr><td>CV (3205)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (4117)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (5566)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>CV (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr></tbody></table>
<table><caption>**Table 21** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Gain ratio (3205)</td><td>0.0044</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Gain ratio (4117)</td><td>0.0029</td><td>0.6585</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0019</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0019</td><td>0.0022</td><td>0.1055</td><td>0.2030</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (3205)</td><td>0.0141</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (4117)</td><td>0.0034</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0019</td><td>0.0048</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0041</td><td>0.0079</td></tr><tr><td>Info gain (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>ReliefF (3205)</td><td>0.0020</td><td>0.0312</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0019</td><td>0.0024</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0019</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0019</td><td>0.0037</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0289</td><td>0.1415</td></tr><tr><td>Significance (3205)</td><td>0.0026</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Significance (4117)</td><td>0.0026</td><td>0.1415</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0019</td><td>0.0020</td><td>0.3822</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0022</td></tr><tr><td>Significance (t = 0)</td><td>0.0019</td><td>0.0057</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0019</td><td>0.0019</td><td>0.0029</td><td>0.0041</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0019</td><td>0.0019</td><td>0.0022</td><td>0.0037</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0019</td><td>0.0019</td><td>0.0026</td><td>0.0073</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0019</td><td>0.0020</td><td>0.0267</td><td>0.0783</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0031</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0026</td><td>0.9794</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0019</td><td>0.0022</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0026</td><td>0.0073</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr></tbody></table>
<table><caption>**Table 21** continued</caption><thead><tr><th></th><th>SpRST (4)</th><th>SpRST (5)</th><th>SpRST (8)</th><th>SpRST (10)</th></tr></thead><tbody><tr><td>Gain ratio (3205)</td><td>0.0044</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Gain ratio (4117)</td><td>0.0029</td><td>0.6585</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (5566)</td><td>0.0019</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Gain ratio (6171)</td><td>0.0019</td><td>0.0022</td><td>0.1055</td><td>0.2030</td></tr><tr><td>Gain ratio (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (3205)</td><td>0.0141</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Info gain (4117)</td><td>0.0034</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (5566)</td><td>0.0019</td><td>0.0048</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Info gain (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0041</td><td>0.0079</td></tr><tr><td>Info gain (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>ReliefF (3205)</td><td>0.0020</td><td>0.0312</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (4117)</td><td>0.0019</td><td>0.0024</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (5566)</td><td>0.0019</td><td>0.0026</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (6171)</td><td>0.0019</td><td>0.0037</td><td>1.0000</td><td>1.0000</td></tr><tr><td>ReliefF (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0289</td><td>0.1415</td></tr><tr><td>Significance (3205)</td><td>0.0026</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Significance (4117)</td><td>0.0026</td><td>0.1415</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Significance (5566)</td><td>0.0019</td><td>0.0020</td><td>0.3822</td><td>1.0000</td></tr><tr><td>Significance (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0022</td></tr><tr><td>Significance (t = 0)</td><td>0.0019</td><td>0.0057</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Sum squares ratio (3205)</td><td>0.0019</td><td>0.0019</td><td>0.0029</td><td>0.0041</td></tr><tr><td>Sum squares ratio (4117)</td><td>0.0019</td><td>0.0019</td><td>0.0022</td><td>0.0037</td></tr><tr><td>Sum squares ratio (5566)</td><td>0.0019</td><td>0.0019</td><td>0.0026</td><td>0.0073</td></tr><tr><td>Sum squares ratio (6171)</td><td>0.0019</td><td>0.0020</td><td>0.0267</td><td>0.0783</td></tr><tr><td>Symmetricaluncert (3205)</td><td>0.0031</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr><tr><td>Symmetricaluncert (4117)</td><td>0.0026</td><td>0.9794</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (5566)</td><td>0.0019</td><td>0.0022</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Symmetricaluncert (6171)</td><td>0.0019</td><td>0.0019</td><td>0.0026</td><td>0.0073</td></tr><tr><td>Symmetricaluncert (t = 0)</td><td>0.0019</td><td>0.0019</td><td>0.0019</td><td>0.0019</td></tr></tbody></table>
## References

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2. Aghdam MH, Ghasem-Aghaee N, Basiri ME (2009) Text feature selection using ant colony optimization. Expert Syst Appl 36(3):6843-6853

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14. Fernández A, del Río S, López V, Bawakid A, del Jesus MJ, Benítez JM, Herrera F (2014) Big data with cloud computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdiscip Rev Data Min Knowl Discov 4(5):380-409

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17. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157-1182

18. https://spark.apache.org/mllib/. Mllib website

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## References

1. Afendi FM, Ono N, Nakamura Y, Nakamura K, Darusman LK, Kibinge N, Morita AH, Tanaka K, Horai H, Altaf-Ul-Amin M et al (2013) Data mining methods for omics and knowledge of crude medicinal plants toward big data biology. Comput Struct Biotechnol J 4(5):1-14

2. Aghdam MH, Ghasem-Aghaee N, Basiri ME (2009) Text feature selection using ant colony optimization. Expert Syst Appl 36(3):6843-6853

3. Ahmed S, Zhang M, Peng L (2013) Enhanced feature selection for biomarker discovery in LC-MS data using GP. In: Evolutionary computation (CEC), 2013 IEEE congress on. IEEE, pp 584-591

4. Asuncion A, Newman DJ (2007) UCI machine learning repository. http://www.ics.uci.edu/~mlearn/MLRepository.html

5. Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Produ Econ 124(1):252-264

6. Bolón-Canedo V, Rego-Fernández D, Peteiro-Barral D, Alonso-Betanzos A, Guijarro-Bertiñas B, Sánchez-Maroño N (2018) On the scalability of feature selection methods on high-dimensional data. Knowl Inf Syst 56(2):395-442

7. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Netw Appl 19(2):171-209

8. Dagdia ZC, Zarges C, Beck G, Lebbah M (2017) A distributed rough set theory based algorithm for an efficient big data pre-processing under the spark framework. In: 2017 IEEE international conference on big data, BigData 2017, Boston, MA, USA, December 11-14, 2017, pp 911-916. https://doi.org/10.1109/BigData.2017.8258008

9. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1-4):131-156

10. Dean J, Ghemawat S (2010) MapReduce: a flexible data processing tool. Commun ACM 53(1):72-77

11. Düntsch I, Gediga G (2000) Rough set data analysis. Encycl Comput Sci Technol 43(28):281-301

12. El-Alfy ESM, Alshammari MA (2016) Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduce. Simul Model Pract Theory 64:18-29

13. Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM sIGKDD Explor News14(2):1-5

14. Fernández A, del Río S, López V, Bawakid A, del Jesus MJ, Benítez JM, Herrera F (2014) Big data with cloud computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdiscip Rev Data Min Knowl Discov 4(5):380-409

15. Ghosh A, Datta A, Ghosh S (2013) Self-adaptive differential evolution for feature selection in hyperspectral image data. Appl Soft Comput 13(4):1969-1977

16. Grzymala-Busse JW, Ziarko W (2000) Data mining and rough set theory. Commun ACM 43(4):108-109

17. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157-1182

18. https://spark.apache.org/mllib/. Mllib website

19. Hu J, Pedrycz W, Wang G, Wang K (2016) Rough sets in distributed decision information systems. Knowl-Based Syst 94:13-22

20. John GH, Kohavi R, Pfleger K et al (1994) Irrelevant features and the subset selection problem. In: Machine learning: proceedings of the eleventh international conference, pp 121-129

21. Larose DT (2014) Discovering knowledge in data: an introduction to data mining. Wiley, New York

22. Lingras P (2001) Unsupervised rough set classification using GAs. J Intell Inf Syst 16(3):215-228

23. Lingras P (2002) Rough set clustering for web mining. In: Fuzzy systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE international conference on, vol 2. IEEE, pp 1039-1044

24. Liu H, Motoda H, Setiono R, Zhao Z (2010) Feature selection: an ever evolving frontier in data mining. In: Feature selection in data mining, pp 4-13

25. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491-502

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**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_43.png" /></figure>

**Zaineb Chelly Dagdia** is a Research and Development Technical Project Manager at National Institute for Research in Computer Science and Automation (Inria). She received her MSc and PhD in Computer Science at the University of Tunis (ISG-Campus), Tunisia, in 2010 and 2014, respectively. After that, she hold the position of a Marie Sklodowska Curie Research Fellow (MSCA-IF-2015-EF, Project No. 702527) at Aberystwyth University, UK (03/2017-02/2019). Her research interests include different aspects of artificial intelligence. She writes on machine learning, data mining and data analytics, evolutionary algorithms and artificial immune systems, big data, and uncertainty theories. She has a strong publication record. She was awarded the Young Researcher First Price (IEEE EHB’2013), the ACM-W Award, the Marie Sklodowska Curie Individual European Fellowship and the Best Reviewer Award (iCDEc 2018). She also acts as a Marie Sklodowska Curie Ambassador, selected as a Female Scientist Role Model, and selected to be among the 200 Heidelberg-Laureate-Forum (HLF) most qualified young researchers.

**Christine Zarges** is a Lecturer in the Department of Computer Science at Aberystwyth University, Wales, UK. She graduated with a PhD in Computer Science from TU Dortmund, Germany, in 2011. Her current main research interests include machine learning, optimization and heuristic search methods.
32. Qian Y, Liang X, Wang Q, Liang J, Liu B, Skowron A, Yao Y, Ma J, Dang C (2018) Local rough set: a solution to rough data analysis in big data. Int J Approx Reason 97:38–63

33. Sakr S, Liu A, Batista DM, Alomari M (2011) A survey of large scale data management approaches in cloud environments. IEEE Commun Surv Tutor 13(3):311–336

34. Schäfer P (2016) Scalable time series classification. Data Min Knowl Discov 30(5):1273–1298

35. Schneider J, Vlachos M (2017) Scalable density-based clustering with quality guarantees using random projections. Data Mining Knowl Discov 31(4):972–1005

36. Shanahan JG, Dai L (2015) Large scale distributed data science using apache spark. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 2323–2324

37. Snir M (1998) MPI-the complete reference: the MPI core, vol 1. MIT Press, Cambridge

38. Talukder N, Zaki MJ (2016) A distributed approach for graph mining in massive networks. Data Mini Knowl Discov 30(5):1024–1052

39. Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9(1):1–12

40. Vinh NX, Chan J, Romano S, Bailey J, Leckie C, Ramamohanarao K, Pei J (2016) Discovering outlying aspects in large datasets. Data Min Knowl Discov 30(6):1520–1555

41. Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

42. Xu X, Jäger J, Kriegel HP (1999) A fast parallel clustering algorithm for large spatial databases. In: High performance data mining. Springer, pp 263–290

43. Zhai T, Gao Y, Wang H, Cao L (2017) Classification of high-dimensional evolving data streams via a resource-efficient online ensemble. Data Mining Knowl Discov 31(5):1242–1265

44. Zhang J, Wang S, Chen L, Gallinari P (2017) Multiple bayesian discriminant functions for high-dimensional massive data classification. Data Min Knowl Discov 31(2):465–501

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_47.png" /></figure>

**Zaineb Chelly Dagdia** is a Research and Development Technical Project Manager at National Institute for Research in Computer Science and Automation (Inria). She received her MSc and PhD in Computer Science at the University of Tunis (ISG-Campus), Tunisia, in 2010 and 2014, respectively. After that, she hold the position of a Marie Sklodowska Curie Research Fellow (MSCA-IF-2015-EF, Project No. 702527) at Aberystwyth University, UK (03/2017-02/2019). Her research interests include different aspects of artificial intelligence. She writes on machine learning, data mining and data analytics, evolutionary algorithms and artificial immune systems, big data, and uncertainty theories. She has a strong publication record. She was awarded the Young Researcher First Price (IEEE EHB’2013), the ACM-W Award, the Marie Sklodowska Curie Individual European Fellowship and the Best Reviewer Award (iCDEc 2018). She also acts as a Marie Sklodowska Curie Ambassador, selected as a Female Scientist Role Model, and selected to be among the 200 Heidelberg-Laureate-Forum (HLF) most qualified young researchers.

**Christine Zarges** is a Lecturer in the Department of Computer Science at Aberystwyth University, Wales, UK. She graduated with a PhD in Computer Science from TU Dortmund, Germany, in 2011. Her current main research interests include machine learning, optimization and heuristic search methods.
**Gaël Beck** is currently Data Scientist at Kameloon. He received his engineer degree in 2015 and his MS degree in Dauphine University, France, in 2015. After three years in Sorbonne Paris Nord University and LIPN Lab (UMR CNRS 7030), he received his PhD degree in Computer Science. His research interests are in the areas of scalable machine learning and data mining with emphasis on clustering and visualization. Find more information on https://github.com/beckgael.

<figure><img src="image_44.png" /></figure>

**Mustapha Lebbah** is currently Associate Professor at the Sorbonne Paris Nord University and a member of Machine learning Team in LIPN Lab (UMR CNRS 7030). His main researches are centered on machine learning (unsupervised learning, mixture model, cluster analysis, scalable machine learning big data and data science). He graduated from USTO University where he received his engineer diploma in 1998. Thereafter, he gained an MSC (DEA) in Artificial Intelligence from the Sorbonne Paris Nord University in 1999. In 2003, after three years in RENAULT R&D, he received his PhD degree in Computer Science from the University of Versailles. He received the "Habilitation Diriger des Recherches" (accreditation to lead research) degree in Computer Science from Sorbonne Paris Nord University in 2012. Mustapha Lebbah is qualified by the French National Council of Universities in Computer Science (section CNU 27) and Statistic (Section CNU 26). Find more information on https://sites.google.com/site/lebbah/.

<figure><img src="image_45.png" /></figure>
<figure><img src="image_48.png" /></figure>

**Gaël Beck** is currently Data Scientist at Kameloon. He received his engineer degree in 2015 and his MS degree in Dauphine University, France, in 2015. After three years in Sorbonne Paris Nord University and LIPN Lab (UMR CNRS 7030), he received his PhD degree in Computer Science. His research interests are in the areas of scalable machine learning and data mining with emphasis on clustering and visualization. Find more information on https://github.com/beckgael.

<figure><img src="image_49.png" /></figure>

**Mustapha Lebbah** is currently Associate Professor at the Sorbonne Paris Nord University and a member of Machine learning Team in LIPN Lab (UMR CNRS 7030). His main researches are centered on machine learning (unsupervised learning, mixture model, cluster analysis, scalable machine learning big data and data science). He graduated from USTO University where he received his engineer diploma in 1998. Thereafter, he gained an MSC (DEA) in Artificial Intelligence from the Sorbonne Paris Nord University in 1999. In 2003, after three years in RENAULT R&D, he received his PhD degree in Computer Science from the University of Versailles. He received the "Habilitation Diriger des Recherches" (accreditation to lead research) degree in Computer Science from Sorbonne Paris Nord University in 2012. Mustapha Lebbah is qualified by the French National Council of Universities in Computer Science (section CNU 27) and Statistic (Section CNU 26). Find more information on https://sites.google.com/site/lebbah/.