# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Comparison Study
Table 8 (see the first line) pointed out the \(p\_value\) calculated between GAK and the filter methods. As displayed, GAK, CFS, and PCA are competitive methods. This is because the hypothesis is not rejected (GAK is statistically different from CFS and PCA) due to the \(p\_value\) not being less than the significance level 0.01. However, GAK is better than Gain Ratio, Mutual Information, and Gini index FS methods, since their \(p\_value\) is less than 0.01. Consequently, GAK outperforms Gain Ratio, Mutual Information, and Gini index, but it is competitive with PCA and CFS. This result is again confirmed with the medians and the means computed in Table 8. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**SVM Accuracy** & **PPFS-GAF (\%)** & **PPFS-GAK (\%)** & **PPFS-TF (\%)** & **PPFS-TK (\%)** & **CFS (\%)** & **PCA (\%)** & **GainRatio (\%)** & **MutualInfo (\%)** & **GiniIndex (\%)** \\ \hline Breast Cancer & 96.17 & 98.56 & 98.56 & 98.56 & 95.80 & 94.96 & 90.90 & 85.65 & 85.65 \\ Liver Disorder & 65.05 & 73.79 & 65.05 & 73.79 & 58.12 & 59.83 & 59.22 & 58.25 & 53.40 \\ Sonar & 82.26 & 85.58 & 86.06 & 87.98 & 90.76 & 93.28 & 53.23 & 77.42 & 77.42 \\ Wine & 96.15 & 96.15 & 96.15 & 96.15 & 63.38 & 78.87 & 80.77 & 88.46 & 88.46 \\ Spam & 93.04 & 93.6 & 93.04 & 93.62 & 85.76 & 86.41 & 83.47 & 96.61 & 96.61 \\ Waveform & 83.79 & 81.65 & 83.79 & 81.45 & 34.43 & 98.36 & 69.85 & 66.18 & 66.18 \\ Vehicle & 83.33 & 83.33 & 79.36 & 83.33 & 65.87 & 73.02 & 53.97 & 48.41 & 55.56 \\ Diabetes & 81.30 & 81.30 & 81.30 & 81.30 & 78.70 & 77.73 & 73.91 & 73.91 & 73.91 \\ Climate & 91.93 & 91.93 & 91.93 & 91.93 & 91.93 & 91.48 & 91.93 & 91.93 & 91.93 \\ WDBC & 95.29 & 98.24 & 98.24 & 98.24 & 95.29 & 93.67 & 90.59 & 91.18 & 91.18 \\ Autralian & 83.50 & 83.50 & 85.92 & 85.92 & 79.13 & 85.51 & 61.65 & 63.59 & 63.59 \\ Stock & 95.42 & 95.42 & 83.10 & 86.62 & 75.00 & 82.74 & 84.15 & 84.50 & 84.50 \\ Glass & 60.32 & 69.84 & 69.84 & 69.84 & 65.07 & 64.49 & 65.08 & 65.08 & 65.08 \\ Segment & 86.58 & 89.47 & 89.47 & 85.14 & 58.83 & 82.29 & 51.80 & 75.90 & 74.31 \\ Heart & 82.72 & 82.72 & 82.72 & 82.72 & 81.85 & 77.78 & 55.56 & 55.56 \\ Ionosphere & 80.77 & 90.38 & 80.77 & 81.73 & 82.69 & 88.03 & 75.00 & 64.42 & 64.42 \\ Colon & 83.33 & 83.33 & 83.33 & 83.33 & 81.73 & 80.65 & - & - & - \\ August S.P & 56.50 & 61.02 & 61.02 & 61.02 & 44.07 & 52.19 & 44.06 & 48.59 & 48.59 \\ Fifia2019 & 60.09 & 60.85 & 60.08 & 60.85 & 60.09 & 60.08 & 60.09 & 60.09 & 60.09 \\ \hline \end{tabular}
\end{table} 
Table 7: The Classification accuracy provided by SVM Classifier after applying the 4 PPFS, CFS, and PCA feature selection techniques. The second comparison study involved comparing PPFS approaches with three wrapper-based FS methods published in [14], named Enhanced Whale Optimization Algorithm (WOA-CM), GA, and PSO using k-NN. Note that GA is also employed in GAF and GAK, and Tribes is one variant of PSO used in TF and TK. Table 9 and Figure 9 show the classification accuracy obtained by k-NN after applying PPFS techniques along with the three wrapper methods using eight UCI datasets. The PPFS approaches outperformed the wrapper FS methods on five datasets. However, only WOA outperformed the four PPFS methods with an improvement of 5.06% for Ionosphere, 13.12% for Colon, and 16.09% for the Sonar datasets. In addition, the three wrapper FS outperformed the four PPFS methods when handling the Colon dataset. The reason behind this success is that WOA is mainly based on the exploration and exploitation concepts leading to the global search. This aspect allowed WOA to find out the optimal solutions for the Waveform, Colon, and Sonar datasets in revealing the important features despite the noise and high correlation containing in these datasets. Since the number of datasets used in this comparison study is small (eight datasets), the result of the statistical tests (Friedman or Wilcoxon) might be inaccurate [48]. Therefore, their results were omitted.