## 7 Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Characteristics** & **Best Method** & **Conf.** & **Lift** & **Lev.** & **Conv.** \\ \hline IR = (\(-\infty\)–48] \& BL\% = (12.56–18.38] & Original & 1.00 & 8.00 & 0.04 & 1.75 \\ IR = (\(-\infty\)–48] \& BL\% = (6.75–12.56] \& OVL\% = (78.8–\(\infty\)) & ROS & 1.00 & 5.00 & 0.04 & 1.60 \\ BL\% = (6.75–12.56] \& OVL\% = (44.4–61.6] & SMOTE & 1.00 & 4.44 & 0.04 & 1.55 \\ IR = (\(-\infty\)–48] \& BL\% = (\(-\infty\)–6.75] \& OVL\% = (44.4–61.6] & SMOTE + TL & 1.00 & 4.44 & 0.04 & 1.55 \\ IR = (48–91] \& OVL\% = (78.8–\(\infty\)) & SMOTE + TL & 1.00 & 4.00 & 0.04 & 1.50 \\ IR = (134–178] & ROS & 1.00 & 2.86 & 0.03 & 1.30 \\ BL\% = (6.75–12.56] \& OVL\% = (78.8–\(\infty\)) & ROS & 1.00 & 2.86 & 0.03 & 1.30 \\ \#Instances = (\(-\infty\)–3007] \& OVL\% = (44.4–61.6] & SMOTE & 1.00 & 1.48 & 0.04 & 1.63 \\ \#Attributes = (\(-\infty\)–22] \& BL\% = (\(-\infty\)–6.75] & SMOTE + CNN & 1.00 & 1.29 & 0.02 & 0.68 \\ IR = (48–91] \& #Attributes = (\(-\infty\)–22] \& SMOTE + OSS & 1.00 & 1.29 & 0.02 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of the best method by means of association rules. On the other hand, the association analysis, in addition to helping us to select the most effective resampling method according to the characteristics of the data, has allowed us to draw some interesting conclusions from the patterns discovered. From the first model, we deduce that the best IBA value is given by the original dataset, without resampling, when IR is very low, regardless of the other properties of the datasets, except in the case that the overlapping between classes is very high, then ROS is the best strategy. It can also be observed that SMOTE used alone or combined with CNN, OOS or TL is the strategy that obtains the best results in most of the rules. Nevertheless, SMOTE + ENN does not reach the best IBA value under any circumstances. In addition, we can mention other representative patterns: If the number of instances is very low, SMOTE is suitable when IR is medium-high while SMOTE + TL is indicated when IR is very low. The association rules also confirm the observation that RUS is the worst strategy since it does not appear in any of the rules of this model. The conclusions from the second rule model are not as clear as in the case of IBA because the effectiveness of resampling strategies varies substantially depending on the metrics being analyzed. Still, some of the conclusions discussed in the case of IBA are applicable here as well. For example, the fact that RUS and SMOTE + ENN do not appear in the rule model as the best methods or that for low IR, the best results are obtained with the original dataset. Finally, to reinforce the validity of the findings and conclusions obtained from this study, despite having worked with the same resampling rates for all datasets, we have analyzed the effect of the resampling methods on the most influential factors on the classification results. Previously in this section, we have pointed out that our work as well as others in the literature have proven that the overlap between classes and the number of instances have the highest impact on the classification of imbalanced data. Regarding the former, the values of class overlapping in the original datasets and in the datasets resampled by means of the different strategies have been plotted against the initial imbalance ratio. By using the fixed resampling rate for all datasets, the imbalance degree after resampling for the datasets with lower original IR will be lower than for the datasets with higher IR. However, as can be seen in Figure 10, this has no direct correlation on the magnitude of the change in class overlapping. Regarding the number of instances, related to the small sample size problem, the values of this factor after resampling depend mainly on the initial size of each dataset.