## 6 Results
### Results from Individual Metrics
The closer AUC is to 1, the farther the classifier is from the random classification, so it will be better. Regarding this measure, given in Figure 2, resampling methods present an irregular behavior, and it can only be clearly appreciated that SMOTE + TL overcomes the others for high imbalance ratios (above 58) while RUS keeps giving bad results. We can also observe from the graphs the decrease of the AUC values as borderline examples and class overlapping increases, irrespective of the resampling strategy applied. Figure 3 displays average precision obtained for minority and majority classes in the experiments. Unlike the previous metrics, RUS showed the best values in all cases while original datasets showed the worst in most of them. The rest of the resampling methods presented an irregular performance. High values of RUS can be explained by the fact that if there are few minority class predictions, the denominator of the precision formula is small and therefore the precision is high even when there are few correct minority class predictions. The improvement of the precision of RUS in case of borderline examples may be caused by the fact that the small sample size caused by RUS reduces noise rate. In  
Figure 1: Influence of dataset characteristics on accuracy for all resampling strategies. the case of overlapping of classes, whereas the overlapping rate increases the precision decreases for all resampling strategies. For recall metric (Figure 4), similar conclusions to those obtained for AUC can be done. SMOTE + TL is the most suitable method for high imbalance ratio, followed by SMOTE, SMOTE + OOS and SMOTE + ENN, which gave very similar results to SMOTE + TL, except in some specific case in which they were worse. SMOTE + ENN is the most indicated for high percentage of borderline examples and for a small number of instances, and ROS for a medium number of instances. RUS method gave again the worst results. F-Measure combines precision and recall since an increase in precision values often occurs at the expense of decreasing recall values. We have used the harmonic measure of precision and recall where both metrics have the same weight in the computation of F-Measure. Its results are shown in Figure 5. SMOTE + TL, and ROS gave the best results for high values of IR (greater than 58) and high overlapping classes, especially when borderline percent is low. SMOTE + OSS had a comparable performance for imbalance ratio greater than 60. It can also be observed that, in general, the F-measure decreases as the number of attributes, number of instances and overlap percentage increase. The values of the next examined metric, G-Mean, are presented in Figure 6. The information provided by this metric is similar as the obtained with F-measure for the best methods, although here the poor performance of RUS is much more evident. Here again, there is a clear decrease in G-Mean for high overlapping values. IBA is a metric that was proposed to overcome the problems of the before metrics by differentiating the contribution of the instances of each class to the accuracy. In Figure 7, showing its values, it can be observed that ROS and SMOTE + TL are the best for an imbalance ratio greater than 54 and in most of the cases of borderline examples. For class overlapping, we also observe that the performance decreases as the number of overlapping class increases. SMOTE + OSS achieved a similar performance for ratios greater than 59. Conclusions for the metric OP (Figure 8) were the same. In both cases RUS was the worst resampling technique. Figure 2: Influence of dataset characteristics on AUC for all resampling strategies. Figure 4: Influence of dataset characteristics on recall for all resampling strategies. Figure 3: Influence of dataset characteristics on precision for all resampling strategies. Figure 5: Influence of dataset characteristics on F-measure for all resampling strategies. Figure 6: Influence of dataset characteristics on G-mean for all resampling strategies.