# Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
## 5 Results and Discussion
### Adult  
The second dataset is the adult dataset [42], also known as the census income dataset. The purpose of this dataset is to predict whether income exceeds 50 K/yr based on census data. The dataset has 32,561 training samples and 16,281 test samples with 14 features. The samples with missing values were removed from the training set and the test set due to their small number. A log-transformation was applied to the features \"capital-gain\" and \"capital-loss\" because of their great range. Then, the numerical features were normalized with a MinMax scaler, while the categorical features were transformed into one-hot encoding. There are two classes in the dataset, one majority and one minority. The imbalance ratio between majority and minority is around 3 to 1. The architecture of the model is the same as the one in the ozone dataset (see Table 2). Table 4 summarizes the results. It is clear that, in most situations, the variants of _BSBS_ have improved the balanced accuracy and F1-macro of the model, with the best F1-macro score being achieved by NBSBS-R(0.2)without any data transformation technique, while the best balanced accuracy was reached by NBSBS-R(0.2) in combination with SMOTE. The ROC-AUC scores are very close to each other without any improvement. The only setup where _BSBS_ was better than its variants was when combined with ClusterCentroids, which show a similarity with the ozone dataset. However, the undersampling methods now seem to work better with the noisy setup and without re-enters unlike the oversampling ones.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset Transformation** & **BSBS(k)** & **Re-Enters** & **Noisy** & **Accuracy** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & **F1-Macro** & **ROC-AUC** \\ \hline None & - & - & - & 96.96 & 51.79 & 52.43 & 88.63 \\  & 0.1 & ✓ & ✓ & 97.13 & 57.26 & 59.86 & 89.32 \\ \hline RUS & - & - & - & 76.45 & 78.24 & 51.87 & 87.96 \\  & 0.1 & ✓ & - & 81.97 & 81.49 & 81.97 & 88.06 \\ \hline ENN & - & - & - & 96.96 & 52.45 & 53.41 & 88.46 \\  & 0.1 & ✓ & - & 97.07 & 62.58 & 64.67 & 89.47 \\ \hline ClusterCentroids & - & - & - & 64.60 & 77.24 & 45.64 & 86.74 \\  & 0.1 & - & - & 64.71 & 77.32 & 45.73 & 87.14 \\ \hline Tomek Links & - & - & - & 96.96 & 52.42 & 53.34 & 88.77 \\  & 0.15 & - & - & **97.18** & 56.73 & 59.54 & 89.07 \\ \hline ROS & - & - & - & 93.77 & 83.60 & 66.79 & 90.26 \\  & 0.1 & ✓ & ✓ & 95.56 & **84.21** & 67.98 & 90.30 \\ \hline SMOTE & - & - & - & 94.15 & 82.52 & 67.38 & 90.19 \\  & 0.2 & ✓ & ✓ & 95.72 & 83.49 & **70.40** & 90.31 \\ \hline Adasyn & - & - & - & 93.93 & 83.15 & 66.79 & 90.21 \\  & 0.2 & - & - & 94.69 & 83.79 & 67.97 & 90.44 \\ \hline Borderline-SMOTE & - & - & - & 94.75 & 80.12 & 67.81 & **90.61** \\  & 0.2 & ✓ & - & 96.10 & 80.30 & 70.20 & 90.40 \\ \hline KMeans-SMOTE & - & - & - & 95.13 & 79.14 & 67.77 & 89.60 \\  & 0.1 & ✓ & ✓ & 95.94 & 77.82 & 69.38 & 89.66 \\ \hline SMOTEENN & - & - & - & 91.34 & 82.44 & 64.50 & 89.86 \\  & 0.15 & ✓ & ✓ & 93.50 & 83.56 & 67.17 & 89.92 \\ \hline SMOTETomek & - & - & - & 94.21 & 82.09 & 66.99 & 90.29 \\  & 0.1 & ✓ & ✓ & 95.18 & 82.87 & 68.13 & 90.32 \\ \hline \hline \end{tabular} Bold indicates the best performance in a specific metric.  
\end{table}
Table 3: Results for the ozone dataset.