# Classification of Imbalanced Data Represented as Binary Features
## 3 Results and Discussion
This result was also confirmed by [33], who reported the same result, namely that the inclusion of resampling methods improves the classification performance of random forest classifier. In the preprocessing task, RSLS is selected as the best oversampling method for all the datasets, while the best feature extraction method varies in each of the datasets. The experimental results strongly confirm our expectation, namely that the \"conversion of binary values of features into numerical values could improve the performance of oversampling\". In most of the table rows in S1 and S2, the values in the rightmost column \"MAX--(no feature extraction)\" are greater than zero. This means that the original performance of an oversampling method tends to be improved by a feature extraction method. For example, the accuracy of the combination (SPECT, RF, no feature extraction, RSLS) was 0.8169. By using feature extraction (t-SNE), it was greatly improved to 0.9829 (in other words, MAX--(no feature extraction) = 0.064). At this point, it can be said that the use of oversampling combined with feature extraction resulted in a good performance. In addition, it should be emphasized that F1-scores showed similar improvement. In many cases, the same combination of feature extraction and oversampling methods achieved best performance in both accuracy and F1-score. Since accuracy and F1-score frequently show a trade-off relationship for imbalanced data, this result indicates that the approach in this study can contribute to the performance improvement of a wide variety of binary feature datasets. Regarding the applicability, it is noticeable that this approach was effective for various ratios of imbalance (from 33:63 to 60:3000) and various numbers of features and samples (the number of features from 10 to 2610 and samples from 42 to 4500). Therefore, it can be said that this method is also effective for both two-class and multiclass classification. One question with regard to the results is the relationship between two performance improvements by feature extraction and oversampling. Are they synergistic or independent? The result indicates that rather than solely using oversampling as the pre-processing task, it is better to use oversampling and feature extraction methods in combination. For instance, the accuracies of the combinations (SPECT, RF, no feature extraction, no oversam \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Evaluation Criteria**} \\ \cline{2-6}  & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F1-Score** \\ \hline
**ring\_100 vs500** & & & & & \\ RF base model & 0.849167 & 0.851203 & 0.778186 & 0.992500 & 0.916437 \\ RF + PCA + RSLS & 0.985000 & 0.987610 & 0.972398 & 0.994500 & 0.991028 \\  & (+0.135833) & (+0.136407) & (+0.194212) & (+0.002000) & (+0.074591) \\ \hline
**ring\_100 vs2000** & & & & & \\ RF base model & 0.951905 & 0.952358 & 0.000000 & 0.999500 & 0.975360 \\ RF + PCA + RSLS & 0.993393 & 0.995200 & 0.955195 & 0.997875 & 0.996536 \\  & (+0.041488) & (+0.042842) & (+0.955195) & (-0.001625) & (+0.021176) \\ \hline
**ring\_60 vs300** & & & & & \\ RF base model & 0.972222 & 0.972222 & NaN & 1.000000 & 0.985915 \\ RF + PCA + RSLS & 0.996042 & 0.997148 & 0.955735 & 0.998786 & 0.997966 \\  & (+0.023820) & (+0.024926) & (NaN) & (\(-0.001214\)) & (+0.012051) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._pling) and (SPECT, RF, no feature extraction, t-SNE) are slightly different (0.8169 and 0.8173, respectively). This means that a simple application of t-SNE to the original data without oversampling does not significantly improve the performance. Despite that, when it is used as a preprocessing algorithm before an oversampling method such as RSLS, it greatly contributed to the improvement of accuracy. Furthermore, we can see many cases in which the simple application of a feature extraction method decreased the performance, but the combined use of it with an oversampling method improved it.