## 4 Results
### Validation of the Proposal
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **OP** & **G-Mean** & **Precision** & **Recall** & **F-Measure** & **AUC** \\ \hline J48 & 0.932 & 0.480 & 0.598 & 0.924 & 0.932 & 0.927 & 0.793 \\ Random Forest & 0.943 & 0.573 & 0.663 & 0.937 & 0.943 & 0.939 & 0.865 \\ Random Tree & 0.916 & 0.597 & 0.681 & 0.925 & 0.916 & 0.920 & 0.720 \\ AdaBoost-J48 & 0.936 & 0.604 & 0.686 & 0.934 & 0.936 & 0.935 & 0.830 \\ AdaBoost-Random Forest & 0.946 & 0.588 & 0.674 & 0.940 & 0.946 & 0.942 & 0.814 \\ AdaBoost-Random Tree & 0.939 & 0.584 & 0.671 & 0.934 & 0.936 & 0.936 & 0.828 \\ Bagging-J48 & 0.939 & 0.505 & 0.615 & 0.931 & 0.939 & 0.934 & 0.849 \\ Bagging-Random Forest & 0.942 & 0.540 & 0.640 & 0.935 & 0.942 & 0.937 & 0.873 \\ Bagging-Random Tree & 0.936 & 0.619 & 0.697 & 0.934 & 0.936 & 0.935 & 0.841 \\ Random Committee-Random Forest & 0.946 & 0.575 & 0.664 & 0.939 & 0.946 & 0.941 & 0.873 \\ Random Committee-Random Tree & 0.934 & 0.591 & 0.676 & 0.932 & 0.934 & 0.933 & 0.817 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the classifiers with smote resampling (500%). The first noteworthy observation in Table 1 is the high accuracy achieved by all classifiers, which exceeds 90%. As discussed above, this fact does not ensure that the classification results for the minority class are good. This can be confirmed by examining the specific metrics for imbalanced data, G-mean and OP, whose values are quite low. The objective of sampling strategies is to increase the value of these metrics without significantly decreasing accuracy. In Tables 2 and 3, we can see that SMOTE, with both 100% and 500% resampling, hardly improves the results of OP and G-mean; in fact, these results worsen in some cases. However, the values of AUC are higher for SMOTE 100%. In order to analyze the behavior of classifiers built from the dataset before and after applying the sampling strategies, the results of the main metrics have been represented in Figures 2-6. Our clustering-RUS approach is represented by the light blue bar. As expected, the accuracy values are lower when  
When comparing RUS and clustering-RUS, the values of OP and G-Mean are similar; RUS is better for some classifiers, but clustering-RUS provides better results for others. However, analyzing accuracy and F-measure, we can observe that the values for clustering-RUS are higher than the values for RUS in most of the cases. Figures 5 and 6 show that the best results of OP and G-Mean with RUS are achieved by the random tree classifier. Nevertheless, as can be seen in Figures 2 and 3, this classifier produces the lowest values in terms of accuracy and F-measure for all datasets, especially when it is applied to data undersampled with RUS. The loss of accuracy for the random tree classifier when using RUS against the original dataset is 6.16%, while the loss yielded when using clustering-RUS only amounts to 4.22%. Figure 4: Area under the receiver operator characteristic (ROC) curve (AUC) obtained by all tested classifiers from the original and resampled datasets. Figure 3: F-measure obtained by all tested classifiers from the original and resampled datasets. Regarding AUC, Figure 4 shows that there are hardly any significant differences between the resampling strategies, although there is a more uniform behavior of clustering-RUS compared to the others. In order to compare the algorithm and to know the significance of the results for OP and G-mean (the metrics specific for imbalanced classification) when using the dataset resampled with the clustering-RUS approach, we performed the Friedman test and a post-hoc analysis based on the Wilcoxon-Holm method. The \(p\)-values obtained in the comparative of the algorithms for OP and G-mean, setting a significance level \(\alpha=0.05\), are given in Tables 6 and 7 respectively. Figure 5: Optimized precision (OP) metric for all tested classifiers from the original and resampled datasets. Figure 6: G-mean metric for all tested classifiers from the original and resampled datasets.