# Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
## 5 Results and Discussion
### Effects on the Convergence  
In principle, the \(BSBS\) algorithm is used to improve the convergence speed of the neural network. With the addition of the three new variations of the algorithm--(\(BSBS\)-\(R\), \(NBSBS\), and \(NBSBS\)-\(R\))--it is imperative to examine how these affect the convergence of the model in general. Usually, the convergence speed is measured the number of epochs it takes to reach a certain score in loss (or accuracy) with respect to the trainset. In order to show that, we will plot the training curve of the accuracy over the epochs of the model. Three data transformation methods were selected for this, ROS, SMOTE and Adasyn. We chose these three because they construct a bigger dataset (oversampling), which will better display the comparison of training speeds. Four setups are compared: training without \(BSBS\), a BSBS(0.1), a NBSBS-R(0.1), and, lastly, a NBSBS-R(0.2). In Figure 2, the training curves are displayed. All BSBS methods are converging much faster, and we can see, in the SMOTE graph, in the middle, that NBSBS-R is a little faster than BSBS at the first epochs,  
\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 & - & - & - & 98.92 & 98.91 & 98.91 & 99.98 \\  & 0.1 & ✓ & - & 99.09 & 99.08 & 99.08 & **99.99** \\ \hline RUS & - & - & - & 96.86 & 96.85 & 96.85 & 99.68 \\  & 0.15 & - & ✓ & 98.08 & 98.08 & 98.07 & 99.97 \\ \hline ENN & - & - & - & 89.21 & 89.24 & 88.42 & 95.09 \\  & 0.1 & ✓ & - & 98.77 & 98.76 & 98.76 & 99.98 \\ \hline ClusterCentroids & - & - & - & 96.02 & 95.99 & 96.03 & 99.90 \\  & 0.2 & - & ✓ & 97.83 & 97.81 & 97.83 & 99.96 \\ \hline Tomek Links & - & - & - & 98.90 & 98.89 & 98.89 & 99.98 \\  & 0.2 & - & ✓ & 99.09 & 99.08 & 99.08 & **99.99** \\ \hline ROS & - & - & - & 99.00 & 98.99 & 98.99 & 99.98 \\  & 0.1 & ✓ & ✓ & **99.17** & **99.16** & **99.16** & **99.99** \\ \hline SMOTE & - & - & - & 98.97 & 98.96 & 98.96 & 99.98 \\  & 0.2 & ✓ & - & 99.12 & 99.11 & 99.11 & **99.99** \\ \hline Adasyn & - & - & - & 98.93 & 98.92 & 98.92 & 99.98 \\  & 0.2 & ✓ & ✓ & 99.10 & 99.09 & 99.09 & **99.99** \\ \hline Borderline-SMOTE & - & - & - & 98.91 & 98.90 & 98.90 & 99.97 \\  & 0.2 & ✓ & - & 99.07 & 99.06 & 99.06 & **99.99** \\ \hline KMeans-SMOTE & - & - & - & 98.90 & 98.89 & 98.89 & 99.98 \\  & 0.2 & ✓ & - & 99.09 & 99.08 & 99.08 & **99.99** \\ \hline SMOTEENN & - & - & - & 80.36 & 80.50 & 78.84 & 89.84 \\  & 0.1 & ✓ & - & 98.79 & 98.78 & 98.78 & 99.98 \\ \hline SMOTETomek & - & - & - & 98.97 & 98.96 & 98.96 & 99.98 \\  & 0.2 & - & ✓ & 99.13 & 99.12 & 99.12 & **99.99** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results for the MNIST Dataset.  
which shows that the variants could be able to achieve better speed at certain situations. Overall, it is clear that the convergence boost that BSBS provides does not decrease when using the variants. On the contrary, it can be beneficial sometimes, although this has to be proven with more experiments and is left as future work.