# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Projection-Pursuit-Based Feature-Selection Techniques

Table 5: The features (outliers) detected by the four proposed PPFS methods for the 19 datasets. Table 6 presents the accuracy, precision, recall, and the F1 values of each classifier before and after applying PPFS methods (columns 3-6 for k-NN, columns 7-10 for SVM, and columns 11-14 for RF). The PPFS methods have globally improved the classification accuracy compared to the accuracy obtained without applying FS. The accuracy obtained using k-NN (resp. SVM and RF) indicates an improvement rate ranging between 0.08 and 6.79 for 9 datasets, while the accuracy for SVM and RF were 0.36-12.9 and 0.01-12.7, respectively, for 16 datasets each. This result is explained by the fact that k-NN is not always efficient with large and noisy datasets. The classification accuracy of the datasets that is not enhanced with k-NN achieved a significant improvement rate with the other classifiers. This is the case for Wine, Spam, August S.P, Diabetes, Australian, Glass, and Ionosphere datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Proposed PPFS** & **Feature Number (Frequency)** & **Outliers Number** & **Time** \\ \hline \multirow{4}{*}{Glass} & PPFS-GAF & 3(51), 7(51) & 2 & 121 ms \\  & PPFS-GAK & 5(100) & 1 & 54 ms \\  & PPFS-TF & 5(100) & 1 & 203 ms \\  & PPFS-TK & 5(100) & 1 & 119 ms \\ \hline \multirow{4}{*}{Segment} & PPFS-GAF & 1(56), 2(56), 14(76), 16(62) & 4 & 937 ms \\  & PPFS-GAK & 1(100), 2(100) & 2 & 470 ms \\  & PPFS-TF & 1(100), 2(99), 12(57), 14(98), 16(85), 17(60) & 6 & 1 s 622 ms \\  & PPFS-TK & 1(99), 2(100) & 2 & 901 ms \\ \hline \multirow{4}{*}{Heart} & PPFS-GAF & 4(98), 5(95), 8(94) & 3 & 96 ms \\  & PPFS-GAK & 4(100), 5(100), 8(100) & 3 & 41 ms \\  & PPFS-TF & 4(100), 5(100), 8(99) & 3 & 161 ms \\  & PPFS-TK & 4(98), 5(100), 8(99) & 3 & 82 ms \\ \hline \multirow{4}{*}{Colon} & PPFS-GAF & 2(62), 6(52), 10(54), 16(55), 24(58), 30(55), 42(64), 46(64), 50(55), 58(52), 64(50), 23(61), 61(67), 71(59), 1612(59), 1754(76) & 17 & 7 s 181 ms \\  & 58(52), 64(50), 236(67), 332(61), 61(679), 71(59), 1612(59), 1754(76) & & & \\ \cline{1-1} \cline{2-4}  & & 1(100), 2(100), 4(100), 6(100), 10(100), 12(100), 14(100), & & & \\ \cline{1-1}  & 16(100), 18(100), 20(100), 22(100), 24(100), 26(100), 28(100), 30(100), & & & \\ \cline{1-1}  & 32(100), 34(99), 36(100), 38(100), 40(100), 42(100), 44(100), 46(100), & & & \\ \cline{1-1}  & PPFS-GAK & 48(99), 50(100), 54(100), 56(100), 58(100), 64(100), 70(100), & 51 & 839 ms \\ \cline{1-1}  & 72(92), 76(99), 78(909), 89(829), 89(84), 86(100), 92(100), & & & \\ \cline{1-1}  & 98(57), 100(57), 102(57), 104(57), 20(699), 236(100), & & & \\ \cline{1-1}  & 610(98), 712(100), 1612(85), 1754(99) & & & \\ \cline{1-1} \cline{2-4}  & & 1(98), 2(96), 49(6), 6(95), 8(95), 10(96), 12(100), 14(94), 16(97), & & \\ \cline{1-1}  & 18(93), 20(96), 22(96), 22(94), 26(95), 26(95), 28(93), 30(96), 32(93), 34(88), & & & \\ \cline{1-1}  & PPFS-TF & 36(93), 38(94), 40(49), 42(95), 44(96), 48(37), 50(59), 54(59), & & 51 & 6 s 443 ms \\ \cline{1-1}  & 56(87), 58(93), 69(92), 64(96), 70(75), 72(76), 76(78), 78(86), & & & \\ \cline{1-1}  & 82(86), 84(73), 86(93), 92(93), 98(52), 100(52), 104(52), & & & \\ \cline{1-1}  & 206(86), 236(97), 332(97), 610(87), 712(95), 1612(66), 1754(88) & & & \\ \hline \multirow{4}{*}{August S.P} & PPFS-GAF & 3-4(100) & 2 & 221 ms \\  & PPFS-GAK & 1(100), 3–4(100) & 3 & 123 ms \\  & PPFS-TF & 1(100), 3–4(100) & 3 & 388 ms \\  & PPFS-TK & 1(100), 3–4(100) & 3 & 274 ms \\ \hline \multirow{4}{*}{Fifa2019} & PPFS-GAF & 5(61), 6(76), 8(83), 9(86), 17(82), 18(84), 80(67) & 7 & 43 s 952 ms \\  & PPFS-GAK & 1(100) & 1 & 21 s 483 ms \\ \cline{1-1}  & PPFS-TF & 1(75), 5(65), 6(79), 8(72), 9(83), 17(75), 18(74), 80(70) & 8 & 44 s 143 ms \\ \cline{1-1}  & PPFS-TK & 1(100) & 1 & 12 s 580 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(Cont.\)