## 4 Results
### Validation of the Proposal
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**G-Mean**} & \multicolumn{3}{c}{**\% G-Mean Loss with Respect**} & \multicolumn{3}{c}{**\% Average Loss of Accuracy**} \\ \cline{2-7}
**Algorithm** & \multicolumn{3}{c}{**tos**} & \multicolumn{3}{c}{**tos**} & \multicolumn{3}{c}{**tos**} & \multicolumn{3}{c}{**and G-Mean**} \\ \cline{2-7}  & **RUS** & **Clustering-RUS** & **RUS** & **Clustering-RUS** & **RUS** & **Clustering-RUS** \\ \hline J48 & 0.63 & 0.61 & 15.05 & 17.79 & 7.85 & 9.53 \\ Random Forest & 0.73 & 0.73 & 2.89 & 2.21 & 2.04 & 1.53 \\ Random Tree & 0.75 & 0.73 & 0.00 & 2.36 & 2.83 & 3.00 \\ AdaBoost-Radsen & 0.74 & 0.72 & 1.51 & 2.92 & 2.49 & 3.08 \\ AdaBoost-Random Forest & 0.72 & 0.72 & 4.22 & 3.05 & 2.71 & 1.73 \\ AdaBoost-Random Tree & 0.72 & 0.73 & 4.22 & 2.36 & 3.69 & 3.00 \\ Bagging-48 & 0.68 & 0.65 & 8.98 & 13.42 & 5.20 & 7.02 \\ Bagging-Random Forest & 0.72 & 0.72 & 4.02 & 3.91 & 2.39 & 1.96 \\ Bagging-Random Tree & 0.74 & 0.74 & 0.90 & 0.56 & 2.41 & 1.61 \\ Random Committee-Random Forest & 0.72 & 0.73 & 3.70 & 2.46 & 2.45 & 1.65 \\ Random Committee-Random Tree & 0.73 & 0.72 & 2.81 & 3.49 & 3.40 & 3.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Loss of G-mean with respect to the best value of G-Mean (0.75), and average loss of accuracy and G-mean for the algorithm-sampling method pairs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**\% Accuracy Loss with Respect to the Best Value**} \\ \cline{2-5}  & **RUS** & **Clustering-RUS** & **RUS** & **Clustering-RUS** \\ \hline J48 & 0.91 & 0.91 & 1.20 & 0.84 \\ Random Tree & 0.87 & 0.89 & 5.65 & 3.64 \\ AdaBoost-Random Forest & 0.91 & 0.92 & 1.20 & 0.42 \\ AdaBoost-Random Tree & 0.89 & 0.89 & 3.15 & 3.64 \\ Bagging-48 & 0.91 & 0.91 & 1.41 & 0.61 \\ Bagging-Random Forest & 0.91 & 0.92 & 0.76 & 0.00 \\ Bagging-Random Tree & 0.88 & 0.90 & 3.91 & 2.65 \\ Random Committee-Random Forest & 0.91 & 0.91 & 1.20 & 0.84 \\ Random Committee-Random Tree & 0.88 & 0.88 & 4.00 & 3.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy and loss of accuracy with respect to the best value of accuracy (0.92) for the algorithm-sampling method pairs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**OP**} & \multicolumn{3}{c}{**\% OF Loss with Respect to the Best Value**} & \multicolumn{3}{c}{**\% Average Loss of Accuracy and OP**} \\ \cline{2-5}  & **RUS** & **Clustering-RUS** & **RUS** & **Clustering-RUS** & **RUS** & **Clustering-RUS** \\ \hline J48 & 0.53 & 0.50 & 24.05 & 28.32 & 12.35 & 14.79 \\ Random Forest & 0.66 & 0.67 & 5.55 & 4.58 & 3.37 & 2.71 \\ Random Tree & 0.70 & 0.67 & 0.65 & 4.26 & 2.85 & 3.95 \\ AdaBoost-J48 & 0.68 & 0.66 & 2.97 & 5.21 & 3.22 & 4.23 \\ AdaBoost-Random Forest & 0.65 & 0.66 & 7.54 & 5.91 & 4.37 & 3.16 \\ AdaBoost-Random Tree & 0.65 & 0.67 & 7.22 & 4.26 & 5.18 & 3.95 \\ Bagging-J48 & 0.60 & 0.55 & 14.78 & 21.62 & 8.10 & 11.11 \\ Bagging-Random Forest & 0.65 & 0.65 & 7.26 & 7.27 & 4.01 & 3.63 \\ Bagging-Random Tree & 0.69 & 0.69 & 2.10 & 1.75 & 3.00 & 2.20 \\ Random Committee-Random Forest & 0.65 & 0.67 & 6.82 & 4.97 & 4.01 & 2.91 \\ Random Committee-Random Tree & 0.67 & 0.66 & 4.86 & 5.95 & 4.43 & 4.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Loss of OP with respect to the best value of OP (0.70), and average loss of accuracy and OP for the algorithm-sampling method pairs. In order to corroborate the conclusions drawn from the tables above, Figure 13 represents the success rates of the minority class (TPR) and those of the majority class (TNR). It is well known that the increase in the former always occurs at the expense of the latter. With this in mind, the first thing that emerges from the examination of the graph is the significant increase in TPR when using undersampling in comparison with the small improvement achieved when using the oversampling strategy.