# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 5 Experimental Results
### Impact of the Sampling Algorithms in the Performance of Associative Classifiers
#### 5.2.3 Impact on the performance of the NAC Classifier  
The AUC results for the NAC classifier are presented below, in Tables 15-17. The good results (AUC improvements) are highlighted in bold, while the results that present less AUC than the original set (imbalanced) are underlined. The oversampling algorithms outperformed the results of using the original data in just five datasets. In all of them, the AUC improvements were of 1% only. Considering that oversampling algorithms increase the computational complexity by augmented the number of instances, we consider that its potential benefits for AUC do not justify its added complexity.  
According to undersampling algorithms, the best results were obtained by NCL and RUS, improving the AUC in four datasets. However, as it can be seen, on numerous occasions, using undersampling techniques negatively impacts the performance of the NAC classifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Original** & **ADASYN** & **ADOMS** & **SMOTE-BL** & **ROS** & **SMOTE-SL** & **SMOTE** \\ \hline Australian & 0.83 & 0.83 & **0.84** & **0.84** & **0.84** & 0.83 \\ Default credit & 0.99 & 0.98 & **1.00** & 0.98 & 0.99 & 0.99 \\ German & 0.67 & 0.65 & 0.67 & 0.67 & 0.66 & 0.67 \\ Give me credit & 0.69 & 0.69 & 0.68 & **0.70** & 0.68 & 0.69 \\ Iranian & 0.61 & 0.59 & 0.60 & 0.61 & 0.61 & 0.60 & 0.59 \\ Japanese & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 \\ Polish\_1 & 0.84 & 0.84 & 0.84 & 0.84 & 0.83 & 0.84 & 0.83 \\ Polish\_2 & 0.60 & **0.61** & **0.61** & **0.61** & **0.61** & **0.61** \\ Polish\_3 & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 \\ Polish\_4 & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 \\ Polish\_5 & 0.50 & 0.50 & 0.50 & **0.51** & 0.50 & 0.50 \\ Qualitative & 0.53 & 0.51 & 0.52 & 0.50 & 0.53 & 0.50 & 0.51 \\ The PAKDD & 0.62 & 0.59 & 0.62 & 0.60 & 0.62 & 0.59 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 15: AUC for NAC classifier after oversampling algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Original** & **SMOTE-ENN** & **SMOTE-TL** & **SPIDER2** & **SPIDER** \\ \hline Australian & 0.84 & 0.83 & **0.85** & **0.86** & **0.85** \\ Default credit & 0.99 & **1.00** & **1.00** & 0.98 & 0.98 \\ German & 0.67 & 0.65 & 0.65 & 0.65 & 0.65 \\ Give me credit & 0.68 & **0.69** & **0.69** & 0.68 & 0.68 \\ Iranian & 0.59 & 0.51 & 0.51 & 0.59 & 0.59 \\ Japanese & 0.68 & 0.63 & 0.63 & 0.67 & 0.67 \\ Polish\_1 & 0.82 & 0.82 & **0.85** & **0.87** & **0.86** \\ Polish\_2 & 0.57 & 0.56 & 0.57 & **0.60** & **0.61** \\ Polish\_3 & 0.76 & 0.50 & 0.50 & 0.76 & 0.76 \\ Polish\_4 & 0.71 & 0.50 & 0.50 & 0.71 & 0.71 \\ Polish\_5 & 0.71 & 0.50 & 0.50 & 0.71 & 0.72 \\ Qualitative & 0.75 & 0.50 & 0.50 & 0.75 & 0.74 \\ The PAKDD & 0.79 & 0.50 & 0.50 & **0.80** & **0.80** \\ \hline \hline \end{tabular}
\end{table}
Table 14: AUC for Extended Gamma classifier after hybrid algorithms.  
As can be seen in Table 17, the hybrid algorithms showed a slight improvement in the performance of the classifier in seven of the analyzed databases. The greatest improvement was obtained in the Iranian dataset, where the Area under the ROC Curve increased from 0.61 to 0.73, with the SMOTE-ENN algorithm. In the rest of the datasets analyzed, at least, strong falls were not observed in terms of classification performance.  
For the NAC classifier, the data balancing algorithms did not show an obvious improvement over the original performance. It should be noted that the algorithms, in addition to involving a computational cost, in hybrid cases and oversampling, also increase the cardinality of the datasets.