## 5 Results Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Original [4]**} & \multicolumn{2}{c}{**100 Repetitions, Test Accuracy**} & \multicolumn{2}{c}{**10 \(\times\) 100 CV**} \\ \cline{2-7}
**Classifier** & **Best (Feat)** & **13 Feat.** & **Worst** & **Average \(\pm\) Std** & **Best** & **Validation** \\ \hline kNN & — & — & 0.6276 & 0.6384 \(\pm\) 0.0066 & 0.6570 & 0.6427 \\ DTC & 0.7877 (19) & 0.7963 & 0.7217 & 0.7479 \(\pm\) 0.0121 & 0.7765 & 0.7409 \\ RFC & 0.7299 (9) & 0.7236 \({}^{\dagger}\) & 0.6765 & 0.6921 \(\pm\) 0.0077 & 0.7158 & 0.6620 \\ ETC & 0.7136 (20) & 0.6857 \({}^{\dagger}\) & 0.6688 & 0.6764 \(\pm\) 0.0064 & 0.6941 & 0.6374 \\ XGBC & 0.7117 (17) & 0.6887 \({}^{\dagger}\) & 0.6688 & 0.6805 \(\pm\) 0.0057 & 0.7040 & 0.6404 \\ \hline \hline \end{tabular} Values in green indicate validation accuracy over the majority rate. \({}^{\dagger}\) Best result reported with 13 features but not necessarily the same 13 features identified as best for DTC.  
\end{table} 
Table 6: Results from [4] compared with the models found in the experimental reproduction with 100 repetitions of the 10-fold CV using the same 13 features. The results in Table 7 show that the 16 features selected by the GA wrapping RFC achieved the highest validation accuracy (71.64%) by using a penalty factor of 0.3, demonstrating that the evolutionary approach can improve the generalization capacity of the models studied, as this result is much higher than those achieved for RFC by using RFE in the previous study. In contrast, the DTC classifier, which had previously obtained the highest test accuracy, experienced a significant drop in validation, evidencing a possible overfitting of the training set, probably because it is using too many features. In the third phase, we test the hypothesis that when using Equation (2), the found feature sets are less prone to overfitting. Table 8 shows the results of the same experiments presented in Table 7 but using the new fitness calculation with a penalization for non homogeneous accuracy among test partitions. The results seem to improve but the differences are small and they cannot be considered conclusive. For this reason, we think that more research is needed to find strategies to avoid overfitting in these challenging high-dimensional datasets with few instances. \begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Classifier** & **noF** & **Penalty** & **Min.** & **Avg. Test Accuracy \(\pm\) Std** & **Max.** & **Validation** \\ \hline \multirow{3}{*}{\(k\)NN} & 10 & 0.3 & 0.6456 & 0.6596 \(\pm\) 0.0091 & 0.6868 & 0.6702 \\  & 11 & 0.5 & 0.6269 & 0.6519 \(\pm\) 0.0091 & 0.6750 & 0.6117 \\  & 8 & 0.7 & 0.6397 & 0.6606 \(\pm\) 0.0095 & 0.6868 & 0.6550 \\ \hline \multirow{3}{*}{DTC} & 25 & 0.3 & 0.7283 & 0.7478 \(\pm\) 0.0100 & 0.7757 & 0.6444 \\  & 25 & 0.5 & 0.7224 & 0.7423 \(\pm\) 0.0099 & 0.7699 & 0.6690 \\  & 12 & 0.7 & 0.7235 & 0.7411 \(\pm\) 0.0082 & 0.7588 & 0.6538 \\ \hline \multirow{3}{*}{RFC} & 16 & 0.3 & 0.7158 & 0.7359 \(\pm\) 0.0090 & 0.7577 & 0.7164 \\  & 9 & 0.5 & 0.6989 & 0.7225 \(\pm\) 0.0085 & 0.7471 & 0.6942 \\  & 7 & 0.7 & 0.7048 & 0.7214 \(\pm\) 0.0074 & 0.7460 & 0.7047 \\ \hline \multirow{3}{*}{ETC} & 18 & 0.3 & 0.6868 & 0.7051 \(\pm\) 0.0103 & 0.7279 & 0.7018 \\  & 12 & 0.5 & 0.6706 & 0.6902 \(\pm\) 0.0114 & 0.7165 & 0.6784 \\  & 9 & 0.7 & 0.6746 & 0.6905 \(\pm\) 0.0079 & 0.7059 & 0.6579 \\ \hline \multirow{3}{*}{XGBC} & 14 & 0.3 & 0.7044 & 0.7216 \(\pm\) 0.0089 & 0.7574 & 0.6690 \\  & 9 & 0.5 & 0.7235 & 0.7447 \(\pm\) 0.0117 & 0.7813 & 0.7035 \\ \cline{1-1}  & 9 & 0.7 & 0.7176 & 0.7356 \(\pm\) 0.0100 & 0.7640 & 0.6965 \\ \hline \hline \end{tabular}  
* Values in green indicate validation accuracy over the majority rate.  
\end{table} 
Table 7: Results using features selected by the genetic algorithm. Figure 5: Evolution of the GA wrapping DTC with a 0.3 feature set penalty and no variance penalty (fourth line of Table 7).