# 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
#### 5.2.4 Impact on the performance of the SNDAM Classifier  
The AUC results for the SNDAM classifier are presented below, in Tables 18-20. The good results (AUC improvements) are highlighted in bold, while the results that present less AUC than the original set (imbalanced) are underlined. Unlike the previously analyzed classifiers, the SNDAM showed an increase in AUC in 11 of the 13 compared datasets, after applying oversampling algorithms. These results point to the benefits of using such sampling techniques to increase SNDAM performance. However, to establish whether such AUC differences are significant or not, Section 5.3 uses statistical test. In addition, undersampling algorithms also seem to increase the AUC of SNDAM, again for 11 of the 13 datasets. NCL, OSS, RUS and TL showed good results, although the increases in AUC were small (1%-4%) except for the Japanese dataset (7%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Original** & **SNTL** & **NCL** & **OSS** & **RUS** & **SBC** & **TL** \\ \hline Australian & 0.83 & **0.84** & 0.83 & 0.83 & **0.84** & 0.82 & 0.83 \\ Default credit & 0.99 & 0.94 & 0.99 & 0.94 & 0.99 & - & 0.99 \\ German & 0.67 & 0.65 & 0.58 & 0.67 & 0.67 & 0.52 & 0.57 \\ Give me credit & 0.69 & 0.64 & 0.68 & 0.68 & 0.69 & 0.68 & 0.69 \\ Iranian & 0.61 & 0.53 & **0.68** & 0.54 & 0.61 & - & **0.62** \\ Japanese & 0.50 & 0.50 & 0.50 & 0.49 & **0.54** & 0.50 & 0.50 \\ Polish\_1 & 0.83 & 0.83 & 0.83 & 0.81 & **0.85** & - & 0.83 \\ Polish\_2 & 0.61 & 0.55 & 0.61 & 0.59 & 0.59 & - & 0.61 \\ Polish\_3 & 0.50 & 0.50 & 0.50 & 0.50 & **0.52** & - & 0.50 \\ Polish\_4 & 0.50 & 0.50 & **0.52** & **0.51** & 0.50 & - & 0.50 \\ Polish\_5 & 0.50 & 0.49 & **0.55** & 0.50 & 0.50 & - & **0.53** \\ Qualitative & 0.53 & **0.54** & 0.53 & 0.53 & 0.52 & - & **0.54** \\ The PAKDD & 0.62 & 0.50 & **0.65** & 0.52 & 0.50 & 0.50 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: AUC for NAC classifier after undersampling algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Original** & **SMOTE-ENN** & **SMOTE-TL** & **SPIDER2** & **SPIDER** \\ \hline Australian & 0.83 & **0.84** & 0.82 & 0.82 & 0.83 \\ Default credit & 0.99 & 0.98 & **1.00** & 0.98 & 0.98 \\ German & 0.67 & 0.60 & 0.58 & 0.51 & 0.51 \\ Give me credit & 0.69 & **0.70** & **0.70** & 0.68 & 0.69 \\ Iranian & 0.61 & **0.73** & **0.65** & 0.61 & **0.62** \\ Japanese & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 \\ Polish\_1 & 0.84 & 0.83 & 0.84 & 0.82 & 0.83 \\ Polish\_2 & 0.60 & **0.61** & **0.61** & **0.61** & **0.61** \\ Polish\_3 & 0.50 & 0.50 & 0.50 & **0.51** & 0.50 \\ Polish\_4 & 0.50 & 0.50 & 0.50 & 0.50 & 0.50 \\ Polish\_5 & 0.50 & **0.53** & **0.52** & **0.51** & 0.50 \\ Qualitative & 0.53 & 0.53 & 0.52 & 0.53 & 0.52 \\ The PAKDD & 0.62 & 0.53 & 0.56 & 0.61 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: AUC for NAC classifier after hybrid algorithms. The hybrid sampling algorithms obtained the best results, being able to increase SNDAM performance in 12 of the 13 datasets (SMOTE-ENN and SMOTE-TL) and in 8 datasets (SPIDER and SPIDER2). For the SNDAM classifier, the data balancing algorithms showed an evident improvement with respect to the original performance, unlike the other associative classifiers analyzed. Again, next section will address the statistical tests to determine if the differences in performance founded are significant or not.