## 4 Results
### Validation of the Proposal
In addition, critical difference diagrams were used to represent the results (Figures 7 and 8). These diagrams show the classifiers ranked by performance. Considering OP and G-mean metrics, the best classifiers are the obtained with bagging-random tree and random committee-random forest, although the thick horizontal line linking all classifiers indicates that the differences between them are not significant. However, it can be observed in the tables that the lowest p-values are obtained when J48 and bagging-random tree are compared with the rest of the algorithms, that is, the greatest differences are obtained for the worst and the best algorithm with respect to the rest. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Algorithm** & **-** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline J48 & 1 & 0.011 & 0.011 & 0.017 & 0.011 & 0.011 & 0.017 & 0.011 & 0.011 & 0.011 & 0.011 \\ Random Forest & 2 & 0.673 & 0.122 & 0.888 & 0.398 & 0.049 & 0.574 & 0.017 & 0.205 & 0.159 \\ Random Tree & 3 & & & 0.159 & 0.325 & 0.575 & 0.049 & 1.000 & 0.011 & 0.778 & 0.673 \\ AdaBoost-148 & 4 & & & & 0.482 & 0.159 & 0.482 & 0.482 & 0.011 & 0.011 & 0.673 \\ AdaBoost-Random Forest & 5 & & & & 0.159 & 0.017 & 0.205 & 0.011 & 0.122 & 0.673 \\ AdaBoost-Random Tree & 6 & & & & & 0.024 & 1.000 & 0.011 & 0.673 & 0.673 \\ Bagging-J48 & 7 & & & & & & 0.011 & 0.011 & 0.017 & 0.205 \\ Bagging-Random Forest & 8 & & & & & & & 0.011 & 0.398 & 0.673 \\ Bagging-Random Tree & 9 & & & & & & & & 0.067 & 0.011 \\ Random Committee-Random Forest & 10 & & & & & & & & & 0.122 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(p\)-values yielded by the statistical tests for the OP metric and the clustering-RUS resampling strategy. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Algorithm** & **-** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline J48 & 1 & 0.018 & 0.018 & 0.042 & 0.018 & 0.018 & 0.028 & 0.018 & 0.018 & 0.018 & 0.018 \\ Random Forest & 2 & & 0.128 & 0.018 & 0.236 & 0.498 & 0.028 & 0.062 & 0.018 & 0.128 & 0.866 \\ Random Tree & 3 & & & 0.028 & 0.125 & 0.063 & 0.397 & 0.063 & 0.018 & 0.063 & 1.00 \\ AdaBoost-148 & 4 & & & & 0.063 & 0.018 & 0.866 & 0.063 & 0.018 & 0.018 & 0.734 \\ AdaBoost-Random Forest & 5 & & & & & 0.611 & 0.028 & 0.236 & 0.018 & 0.498 & 0.866 \\ AdaBoost-Random Tree & 6 & & & & & & 0.063 & 0.397 & 0.018 & 0.128 & 0.866 \\ Bagging-J48 & 7 & & & & & & & 0.018 & 0.028 & 0.176 \\ Bagging-Random Forest & 8 & & & & & & & & 0.018 & 0.866 & 0.735 \\ Bagging-Random Tree & 9 & & & & & & & & 0.018 & 0.236 \\ Random Committee-Random Forest & 10 & & & & & & & & & 0.236 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(p\)-values yielded by the statistical tests for the G-mean metric and the clustering-RUS resampling strategy. Figure 8: Critical difference diagram to compare the classifiers regarding the G-mean metric for the clustering-RUS resampling strategy. Figure 7: Critical difference diagram to compare the classifiers regarding the OP metric for the clustering-RUS resampling strategy. Since the objective of this work is to prove the improvement of the algorithm performance when using our clustering-based resampling proposal, we have applied the same significance tests to compare the four best classifiers induced from the datasets resampled with both RUS and clustering-RUS, which are the resampling techniques that have shown the best behavior in the experiments. Figures 9 and 10 show the results, which evidence the better performance of all the algorithms with clustering-RUS (C-RUS in the figures) vs. RUS. Another conclusion that can be deduced from the graphs in Figures 2-6 is that the increase in the values of OP and G-mean for classifiers used with RUS and clustering-RUS is accompanied by the decrease in the values of accuracy and F-measure. Therefore, it would be interesting to find the classifier-sampling method pair that provides the best balance between those metrics.