# 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
#### 5.2.2 Impact on the performance of the Extended Gamma Classifier  
The AUC results for the Extended Gamma classifier are presented in Tables 12-14 below. The good results (AUC improvements) are highlighted in bold, while the results that present less AUC than the original set (imbalanced) are underlined.  
For the Extended Gamma classifier, the results of oversampling algorithms were similar to those obtained by the HACT classifier. There is no clear advantage of applying the oversampling algorithms, in the performance of the classifier.  
For the undersampling algorithms, there is an improvement of classifier performance after applying OSS and CNNTL in six and five of the compared datasets, respectively. To determine if these differences in performance are significant or not, statistical tests were applied (Section 5.3).  
For the Extended Gamma classifier, the hybrid algorithms showed a subtle improvement in performance in some datasets (ex. Australian, Default credit, Give me credit, Polish_1 and Polish_2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Original** & **CNNTL** & **NCL** & **OSS** & **RUS** & **SBC** & **TL** \\ \hline Australian & 0.84 & 0.84 & **0.87** & 0.84 & 0.83 & 0.84 & **0.85** \\ Default credit & 0.99 & 0.94 & 0.99 & 0.94 & 0.99 & - & 0.99 \\ German & 0.67 & **0.69** & 0.66 & **0.68** & 0.67 & 0.53 & 0.66 \\ Give me credit & 0.68 & 0.68 & 0.68 & **0.69** & 0.68 & 0.67 & **0.70** \\ Iranian & 0.59 & **0.60** & 0.59 & **0.60** & 0.59 & - & 0.59 \\ Japanese & 0.68 & **0.70** & 0.68 & **0.69** & 0.67 & 0.50 & 0.68 \\ Polish\_1 & 0.82 & **0.85** & **0.86** & **0.83** & 0.82 & - & **0.85** \\ Polish\_2 & 0.61 & 0.60 & 0.60 & 0.61 & 0.61 & - & 0.61 \\ Polish\_3 & 0.76 & 0.76 & 0.75 & 0.74 & 0.75 & - & 0.76 \\ Polish\_4 & 0.71 & 0.68 & 0.71 & 0.69 & 0.70 & - & 0.71 \\ Polish\_5 & 0.71 & **0.72** & 0.71 & **0.72** & 0.71 & - & 0.71 \\ Qualitative & 0.75 & 0.73 & 0.75 & 0.73 & 0.74 & - & 0.75 \\ The PAKDD & 0.79 & 0.78 & 0.79 & 0.79 & 0.79 & 0.55 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 13: AUC for the Extended Gamma classifier after undersampling algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Original** & **ADASYN** & **ADOMS** & **SMOTE-BL** & **ROS** & **SMOTE-SL** & **SMOTE** \\ \hline Australian & 0.84 & **0.85** & 0.83 & **0.85** & 0.83 & 0.83 & 0.83 \\ Default credit & 0.99 & 0.98 & 0.99 & 0.97 & 0.99 & 0.99 & 0.99 \\ German & 0.67 & 0.63 & 0.61 & 0.64 & 0.67 & 0.67 & 0.64 \\ Give me credit & 0.68 & 0.67 & 0.54 & 0.67 & **0.69** & **0.69** & 0.65 \\ Iranian & 0.59 & 0.51 & 0.50 & 0.52 & 0.59 & 0.59 & 0.51 \\ Japanese & 0.68 & 0.63 & 0.57 & 0.58 & 0.67 & 0.64 & 0.63 \\ Polish\_1 & 0.82 & **0.85** & **0.83** & **0.85** & 0.82 & 0.82 & 0.82 \\ Polish\_2 & 0.57 & 0.55 & 0.50 & **0.60** & **0.61** & **0.58** & **0.61** \\ Polish\_3 & 0.76 & 0.50 & 0.50 & 0.50 & 0.76 & 0.58 & 0.50 \\ Polish\_4 & 0.71 & 0.50 & 0.50 & 0.50 & 0.71 & 0.55 & 0.50 \\ Polish\_5 & 0.71 & 0.50 & 0.50 & 0.50 & **0.72** & 0.62 & 0.50 \\ Qualitative & 0.75 & 0.50 & 0.50 & 0.50 & 0.75 & 0.67 & 0.50 \\ The PAKDD & 0.79 & 0.50 & 0.50 & 0.51 & 0.78 & 0.71 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 12: AUC for the Extended Gamma classifier after oversampling algorithms.  
In the remaining of the datasets, differences can be seen in favor of the AUC are less than 1%. However, the SMOTE-ENN and SMOTE-TL algorithms had very unfavorable results in bankruptcy detection, evidencing a loss of more than 20% of AUC, with respect to the original.  
Same as for the HACT classifier, in the case of the Extended Gamma classifier, for the over-sampling algorithms, the slight improvements in performance obtained do not justify, in the authors' criteria, the increase in the cardinality of datasets.