# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Projection-Pursuit-Based Feature-Selection Techniques
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**PPFS**} & \multicolumn{4}{c}{**k-NN**} & \multicolumn{4}{c}{**SVM**} & \multicolumn{4}{c}{**RF**} \\ \cline{3-14}  & & **Accu** & **Prec** & **Rec** & **F1** & **Accu** & **Prec** & **Rec** & **F1** & **Accu** & **Prec** & **Rec** & **F1** \\ \hline \multirow{3}{*}{Breast C.} & Without & 95.22 & 95.68 & 97.08 & 96.38 & 98.09 & 99.26 & 97.81 & 98.53 & 94.64 & 97.73 & 94.16 & 95.91 \\  & PPFS-GAF & 97.61 & 97.83 & 98.54 & 98.18 & 96.17 & 97.08 & 97.08 & 97.08 & 95.22 & 96.35 & 96.35 & 96.35 \\  & PPFS-GAF/PPFS-TF/PPFS-TK & 95.22 & 95.68 & 97.08 & 96.38 & 98.56 & 100 & 97.81 & 98.89 & 97.61 & 98.53 & 97.81 & 98.17 \\ \hline \multirow{3}{*}{Sonar} & Without & 70.97 & 90.27 & 90.59 & 54.20 & 69.35 & 66.67 & 84.85 & 74.67 & 82.82 & 84.38 & 81.82 & 83.08 \\  & PPFS-GAF & 75.81 & 71.43 & 90.91 & 80.00 & 82.26 & 80.56 & 89.68 & 47.32 & 88.71 & 88.24 & 90.91 & 89.55 \\  & PPFS-GAF & 66.13 & 63.64 & 84.85 & 72.73 & 80.65 & 81.28 & 88.00 & 48.18 & 72.58 & 76.67 & 69.70 & 73.02 \\  & PPFS-TF & 75.81 & 71.43 & 90.91 & 80.00 & 80.65 & 83.87 & 85.64 & 49.48 & 80.65 & 80.00 & 84.85 & 82.35 \\  & PPFS-TK & 67.74 & 64.44 & 87.88 & 74.36 & 69.35 & 68.42 & 94.61 & 41.97 & 80.65 & 86.21 & 75.76 & 80.65 \\ \hline \multirow{3}{*}{Liver D.} & Without & 63.11 & 64.86 & 80.00 & 71.64 & 76.70 & 75.00 & 90.00 & 81.82 & 63.11 & 67.74 & 70.00 & 68.85 \\  & PPFS-GAF & 69.90 & 68.85 & 90.00 & 77.70 & 65.05 & 67.14 & 78.33 & 72.31 & 71.84 & 75.41 & 76.67 & 76.03 \\  & PPFS-TK & 66.02 & 65.43 & 88.33 & 75.18 & 73.79 & 73.91 & 85.00 & 79.07 & 71.84 & 75.41 & 76.67 & 76.03 \\  & PPFS-TK & 66.02 & 65.43 & 88.33 & 75.18 & 73.79 & 73.91 & 85.00 & 79.70 & 71.84 & 75.41 & 76.67 & 76.03 \\ \hline \multirow{3}{*}{Wine} & Without & 98.08 & 98.41 & 97.98 & 98.04 & 92.31 & 91.69 & 93.09 & 92.26 & 98.08 & 98.41 & 98.15 & 98.23 \\  & PPFS-GAF & 98.08 & 98.41 & 98.15 & 98.23 & 96.15 & 95.24 & 97.10 & 95.92 & 100 & 100 & 100 & 100 \\  & PPFS-GAF/PPFS-TF/PPFS-TK & 96.15 & 96.83 & 96.49 & 96.48 & 96.15 & 96.03 & 96.49 & 96.10 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{Spam} & Without & 90.72 & 92.45 & 92.22 & 92.34 & 92.68 & 93.49 & 94.50 & 93.99 & 96.01 & 95.89 & 97.61 & 96.74 \\  & PPFS-GAF & 88.69 & 90.09 & 91.39 & 90.74 & 93.04 & 93.92 & 95.69 & 94.34 & 93.76 & 93.50 & 96.41 & 94.94 \\  & PPFS-GAF & 89.41 & 90.59 & 92.11 & 91.34 & 93.62 & 93.90 & 95.69 & 94.79 & 95.00 & 94.75 & 97.13 & 95.92 \\  & PPFS-TF & 90.72 & 90.78 & 94.26 & 92.49 & 93.04 & 97.41 & 94.26 & 94.48 & 95.43 & 95.52 & 97.01 & 96.26 \\  & PPFS-TK & 89.99 & 90.49 & 93.90 & 91.87 & 93.94 & 93.53 & 95.10 & 94.31 & 95.36 & 94.99 & 97.49 & 96.22 \\ \hline \multirow{3}{*}{Waveform} & Without & 86.06 & 83.95 & 84.52 & 83.77 & 86.39 & 86.39 & 86.38 & 86.37 & 85.46 & 85.41 & 85.84 & 85.35 \\  & PPFS-GAF/PPFS-TF & 82.45 & 82.38 & 83.51 & 81.89 & 83.79 & 83.76 & 83.77 & 83.67 & 80.65 & 80.59 & 80.86 & 80.24 \\  & PPFS-GAF & 82.12 & 82.07 & 82.85 & 81.48 & 81.65 & 81.63 & 81.55 & 81.57 & 81.12 & 78.89 & 79.04 & 78.50 \\  & PPFS-TK & 81.99 & 81.90 & 83.80 & 81.32 & 81.45 & 81.42 & 81.36 & 81.35 & 81.78 & 81.72 & 81.98 & 81.49 \\ \hline \multirow{3}{*}{Vehicle} & Without & 67.46 & 67.84 & 65.92 & 66.73 & 79.76 & 80.10 & 79.74 & 79.75 & 73.02 & 73.41 & 71.57 & 72.35 \\  & PPFS-GAF/PPFS-GAK/PPFS-TK & 70.24 & 70.61 & 69.73 & 70.13 & 83.33 & 83.52 & 83.10 & 83.27 & 74.60 & 74.98 & 73.67 & 74.10 \\  & PPFS-TF & 73.02 & 73.30 & 72.04 & 72.40 & 79.36 & 79.57 & 78.81 & 79.10 & 76.98 & 77.28 & 76.00 & 76.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The classification results obtained after (and before) applying the PPFS approaches using KNN, SVM, and RF classifiers for the 19 datasets. The PPFS methods also managed well for the Fifa2019 dataset even though the accuracy remains below 70%. This dataset is large and required massive preprocessing efforts.