## 3 Results and Discussion
### Results Corresponding to All the Datasets
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **RF** & \multicolumn{2}{c}{**SVM-L**} & \multicolumn{2}{c}{**SVM-G**} \\ \hline
**Method** & **Rank** & **Method** & **Rank** & **Method** & **Rank** \\ \hline RF:t & 4.676 & SVM-L:All & 4.765 & SVM-G:OR100 & 5.397 \\ RF:All & 4.691 & SVM-L:Ot & 5.059 & SVM-G:Ot & 5.647 \\ RF:Ot & 4.941 & SVM-L:OR1000 & 5.515 & SVM-G:All & 5.647 \\ RF:T & 5.500 & SVM-L:t & 5.529 & SVM-G:t & 5.868 \\ RF:OT & 5.750 & SVM-L:OR100 & 6.000 & SVM-G:O & 5.941 \\ RF:OR1000 & 6.456 & SVM-L:R1000 & 6.294 & SVM-G:OB & 5.941 \\ RF:OR100 & 6.500 & SVM-L:OB & 6.412 & SVM-G:OR1000 & 6.132 \\ RF:OB & 7.015 & SVM-L:O & 6.956 & SVM-G:RO1000 & 6.471 \\ RF:R1000 & 7.162 & SVM-L:OT & 6.956 & SVM-G:OT & 6.985 \\ RF:O & 7.515 & SVM-L:R100 & 7.426 & SVM-G:T & 7.265 \\ RF:R100 & 8.412 & SVM-LT & 7.662 & SVM-G:R100 & 7.735 \\ RF:B & 9.382 & SVM-L:B & 9.426 & SVM-G:B & 8.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average ranks for each classification method. Figure 4: Boxplots of the differences in accuracy between the different feature sets and the feature set (OB), for the three considered classifiers. The boxplots on the right do not include the outliers. The average differences are marked with a red dot (*). The use of the Nemenyi test [44] was also proposed by [PERSON] [41] to compare methods in a pairwise way. For a certain level of confidence (\(\alpha\)), the test determines a critical difference (CD) value. If the difference between the average rankings of two methods is greater than CD, the null hypothesis, \(H_{0}\), that both methods have equal performance, is rejected. Figure 5 shows the [PERSON]'s CD diagrams. In these diagrams, thick horizontal lines are used to connect methods whose difference in average ranks is smaller than CD. The methods were also compared using the Bayesian Signed-Rank Test [43], the Bayesian framework equivalent version of the Wilcoxon Signed-Rank Test. In this test, the value of the Region of Practical Equivalence (ROPE) was set to 1% for accuracy. Two methods were considered equivalent when the difference in their performance was smaller than this ROPE value. The test determines three probability values, corresponding to the following cases: (1) one method is better than the other; (2) vice versa; (3) they are in the ROPE. Figures 6-8 show the Bayesian Signed-Rank Tests posteriors, for the RF, SVM-L, and SVM-G classifiers, respectively. In these figures, the (OB) feature set is compared against each one of the other feature sets, for the corresponding classifier. For each feature set, there is a triangle. In these triangles [43], the bottom-left and bottom-right regions correspond to the case where one method is better than the other or vice versa. The top region represents the case where the ROPE is more probable. The corner triangles show the probability of each region. The left region in the triangle is for OB and the right region for the other feature set. Figure 6 shows that, for RF, the feature set with more favorable results when compared to (OB) is (All), with a probability of 0.767, while it is 0.000 for (OB). Figure 7 shows that, for SVM-L, the best feature set is (Ot): its probability is 0.821, while it is 0.000 for (OB). In Figure 8, for SVM-G, the results are less favorable for the alternative cases to (OB). The best feature set is (Ot), with a probability of 0.244, being 0.001 the probability for (OB). The classification results for the three different classifiers therefore show an improvement that can be considered as significant, when using the different types of proposed feature sets, versus the features proposed in [18]. Figure 5: Critical difference diagrams for the Nemenyi test (\(\alpha=0.05\)). Figure 6: Posteriors for the Bayesian sign-rank tests for RF, from all the datasets. Figure 7: Posteriors for the Bayesian sign-rank tests for SVM-L, from all the datasets.