# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 5 Experimental Results
### Results of the Sampling Algorithms  
As expected, the oversampling algorithms were able to perfectly balance the datasets, obtaining and imbalance ratio of one in all cases (Table 6). Considering the experiments performed, we can conclude that oversampling methods tend to obtain perfect balances. However, these results are at the cost of artificially increasing the cardinality of the data sets. The next section will analyze how these results impact the performance of classifiers of the associative approach, and if this computational cost is justified in results of better performance. Below, the results of the undersampling algorithms are offered in Table 7, for each of the datasets analyzed. The cases in which the classes were inverted are shown in italics (the minority class became the majority), and in bold the good results (those with an imbalance closer to 1). The RUS method obtained a perfectly balanced dataset, in all cases. CNN obtained good results for five datasets, but inverting the classes in two of them. It also has an interesting behavior for the Australian, Japanese and Qualitative datasets (original IR of 1.25, 1.26 and 1.34, respectively). In these datasets, the CNN method inverted the classes, and increase the imbalance ratio (to 3.37, 3.45 and 6.36, respectively). In the remaining datasets, CNN obtained IR from 1.12 to 2.62. The CNNTL algorithm maintain the behavior of CNN in the Australian, Japanese and Qualitative datasets (returning IRs of 8.59, 9.07 and 5.85, respectively). In the remaining datasets, is had good performances (from 1.15 to 2.96), but inverting the classes in the Default credit, German and Give me credit datasets. NCL algorithm obtained good results for the datasets having an original imbalance ratio lover than four, and it did not obtain a balanced dataset in the remaining ones. It also inverted the classes in the Australian, Japanese and Qualitative datasets. \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 & 1.25 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Default credit & 3.52 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ German & 2.33 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Give me credit & 13.96 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Iranian & 19.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Japanese & 1.26 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Polish\_1 & 24.93 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Polish\_2 & 24.43 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Polish\_3 & 20.22 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Polish\_4 & 18.01 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Polish\_5 & 13.41 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Qualitative & 1.34 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ The PAKDD & 4.12 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Imbalance ratio for oversampling algorithms. The OSS algorithm had the same behavior of CNN and CNNTL in the Australian, Japanese and Qualitative datasets (with IRs of 5.50, 5.30 and 6.27, respectively). In the remaining datasets, it obtained good results (IRs from 1.21 to 2.56). Same as CNNTL, it inverted the classes in the Default credit, German and Give me credit datasets. SBC algorithm had a disastrous behavior for the financial data. It inverted the classes in all datasets, and in nine cases it deleted the entire majority class (results marked with -). TL had good results for the almost balanced datasets (IR \(<\) 2), and did not obtained balanced results in the remaining data. In general, the balancing methods evaluated but RUS had a poor performance. CNN, CNNTL and OSS obtained highly imbalanced results for almost balanced data (IR \(<\) 2). We consider that those methods should not be applied to balanced or near balanced data. For the remaining datasets, their results range from 1.12 to 2.62 (CNN), 1.15 to 2.96 (CNNTL) and 1.21 to 2.56 (OSS).