# Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
## 5 Results and Discussion
### Default of Credicard Clients  
Another tabular dataset that was used in this work is default of creditcard clients [42]. This dataset aimed at the case of customer default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. The dataset contains 30,000 samples with 24 features. Similarly, with the ozone level detection dataset, this dataset does not have a specified test set, so a stratified five-fold cross-validation was also employed. There are two classes with a big difference in samples per class, resulting in an imbalance ratio of 3.5 to 1. The architecture of the model can be seen in Table 5.  
One-hot encoding and feature scaling (standardization) was also applied. Table 6 exhibits the results of the experiments of this dataset. The variants of \(BSBS\) seem to have achieved better performance than its original version, except from SMOTE, where \(BSBS(0.2)\) performed the best. Regarding overall performance, RUS combined with \(BSBS\)-\(R(0.1)\) reached the highest balanced accuracy, while ENN with \(BSBS\)-\(R(0.15)\) achieved the highest F1-macro score.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset Transformation** & **BSBS(k)** & **Re-Enters** & **Noisy** & **Accuracy** & **Balanced Accuracy** & **F1-Macro** & **ROC-AUC** \\ \hline None & - & - & - & **84.41** & 78.39 & 78.43 & 89.82 \\  & 0.2 & ✓ & ✓ & 84.01 & 79.83 & **78.52** & 89.57 \\ \hline RUS & - & - & - & 81.20 & 81.35 & 77.21 & 89.69 \\  & 0.1 & - & ✓ & 82.35 & 81.41 & 77.59 & 89.47 \\ \hline ENN & - & - & - & 82.31 & 81.38 & 77.83 & 89.65 \\  & 0.1 & - & ✓ & 82.51 & 81.14 & 77.72 & 89.57 \\ \hline ClusterCentroids & - & - & - & 77.96 & 79.31 & 74.08 & 86.90 \\  & 0.15 & - & - & 78.24 & 79.22 & 74.47 & 86.39 \\ \hline Tomek Links & - & - & - & 84.38 & 78.94 & 78.39 & 89.78 \\  & 0.15 & ✓ & ✓ & 83.71 & 80.47 & 78.10 & 89.52 \\ \hline ROS & - & - & - & 80.74 & 81.33 & 76.81 & 89.84 \\  & 0.2 & ✓ & ✓ & 82.01 & 81.04 & 77.13 & 89.63 \\ \hline SMOTE & - & - & - & 81.16 & 81.23 & 77.14 & 89.66 \\  & 0.2 & ✓ & ✓ & 82.55 & **81.87** & 77.26 & 89.52 \\ \hline Adasyn & - & - & - & 79.26 & 80.96 & 75.79 & 89.47 \\  & 0.15 & ✓ & ✓ & 81.48 & 80.92 & 76.45 & 89.39 \\ \hline Borderline-SMOTE & - & - & - & 78.49 & 80.91 & 75.02 & 89.06 \\  & 0.15 & ✓ & - & 80.68 & 81.09 & 76.08 & 89.19 \\ \hline KMeans-SMOTE & - & - & - & 82.26 & 80.23 & 77.42 & 89.33 \\  & 0.15 & ✓ & ✓ & 82.43 & 80.42 & 77.58 & **89.89** \\ \hline SMOTEENN & - & - & - & 81.36 & 81.17 & 76.94 & 89.53 \\  & 0.15 & ✓ & - & 80.50 & 81.28 & 76.98 & 89.37 \\ \hline SMOTETomek & - & - & - & 80.69 & 81.33 & 76.83 & 89.62 \\  & 0.1 & ✓ & - & 82.23 & 81.30 & 77.34 & 89.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for the Adult dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Units** & **Activation** \\ \hline Dense & 100 & ReLU \\ Dense & 30 & ReLU \\ Dropout (0.5) & - & - \\ Dense & 2 & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model architecture for the default of credicard clients dataset (Dense model).