# Classification of Imbalanced Data Represented as Binary Features
## 3 Results and Discussion  
In this experiment, we tested the combinations of six datasets, four classifiers, five feature extraction methods including \"no feature extraction\", and eight oversampling methods including \"no oversampling\". Due to the limitations of the data and software, some combinations were omitted from the experiments. For instance, ICA, BLS, and ANS were not tested for the _C. difficile_ pathogenicity dataset. All the results and the summary of them are shown in Supplementary Materials S1, S2, and S3. Table 2 shows the results of the 10 datasets used in this study based on the evaluation metrics of accuracy, sensitivity, specificity, precision, and F1-score. The results of the base model compared to the best combination of feature extraction and the oversampling method are reported on the table. The values are the average of 3-, 5-, or 10-fold cross-validation based on the datasets. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Evaluation Criteria**} \\ \cline{2-6}  & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F1-Score** \\ \hline
**SPECT** & & & & & \\ RF base model & 0.816896 & 0.901468 & 0.490909 & 0.872211 & 0.886598 \\ RF + t-SNE + RSLS & 0.982938 & 0.991090 & 0.951515 & 0.987467 & 0.989275 \\  & (+0.166042) & (+0.089622) & (+0.460606) & (+0.115256) & (+0.102677) \\ \hline
**analcatdata\_fraud** & & & & & \\ RF base model & 0.690476 & 0.247863 & 0.888889 & 0.500000 & 0.331429 \\ RF + UMAP + RSLS & 0.97619 & 0.940171 & 0.992337 & 0.982143 & 0.960699 \\  & (+0.285714) & (+0.692308) & (+0.103447) & (+0.482143) & (+0.62927) \\ \hline
**car\_evaluation** & & & & & \\ RF base model & 0.954138 & 0.954138 & 0.984713 & 0.954138 & 0.954138 \\ RF + PCA + RSLS & 0.995804 & 0.995804 & 0.998601 & 0.995804 & 0.995804 \\  & (+0.041666) & (+0.041666) & (+0.013888) & (+0.041666) & (+0.041666) \\ \hline
**MRSA pathogenicity** & & & & & \\ RF base model & 0.617187 & 0.873016 & 0.128788 & 0.656716 & 0.749574 \\ RF + ICA + RSLS & 0.979167 & 0.988095 & 0.962121 & 0.980315 & 0.98419 \\  & (+0.36198) & (+0.115079) & (+0.83333) & (+0.323599) & (+0.234616) \\ \hline
**MRSA drug resistance** & & & & & \\ RF base model & 0.906915 & 0.644737 & 0.973333 & 0.859649 & 0.736842 \\ RF + t-SNE + RSLS & 0.99734 & 0.986842 & 1.000000 & 1.000000 & 0.993377 \\  & (+0.090425) & (+0.342105) & (+0.02667) & (+0.140351) & (+0.256535) \\ \hline _C. difficile pathogenicity_ & & & & & \\ RF base model & 0.750361 & 0.727599 & 0.765700 & 0.676667 & 0.701209 \\ RF + UMAP + RSLS & 0.984127 & 0.982079 & 0.985507 & 0.982014 & 0.980322 \\  & (+0.233766) & (+0.25448) & (+0.219807) & (+0.305347) & (+0.279113) \\ \hline
**ring\_1500 vs300** & & & & & \\ RF base model & 0.750250 & 0.770829 & 0.648922 & 0.915357 & 0.836897 \\ RF + PCA + RSLS & 0.971700 & 0.982621 & 0.946772 & 0.976857 & 0.979726 \\  & (+0.22145) & (+0.211792) & (+0.297850) & (+0.061500) & (+0.142829) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of the best combination compared to base model. The best combination is selected based on the highest value of the sum of the accuracy and F1-score. As can be seen in Table 2, it can be observed that most of the metrics outperformed the base model without the feature extraction and oversampling methods as the preprocessing task. This strongly confirms our hypothesis that the combination of feature extraction and the oversampling method can improve the performances of classifiers. This approach can be applied to the imbalanced binary feature dataset with different ratios of imbalance from low to high (33:63 to 60:3000). In the results, the best combination of all datasets is obtained by random forest classifier. Random forest is said to be a classifier that is prone to class imbalance, but when it is combined with feature extraction methods and oversampling techniques, it outperformed the other classifiers with the same combination.