Table 5 Performance of the Naïve Bayes classifier using different resampling techniques, reported as mean ± standard deviation across all benchmark datasets. The table includes evaluation metrics such as AUC, F1-score, G-mean, Recall, and Precision. The proposed Borderline-Shifting Oversampling (BSO) method demonstrates noticeable performance gains over existing resampling methods, indicating its effectiveness in enhancing classification of minority classes while maintaining overall model stability.
From: An approach for handling imbalanced datasets using borderline shifting
Technique | F1-score | G-mean | Precision | Recall | AUC |
|---|---|---|---|---|---|
Without | 0.88 ± 0.16 | 0.52 ± 0.09 | 0.45 ± 0.05 | 0.52 ± 0.06 | 0.81 ± 0.15 |
NearMiss | 0.54 ± 0.11 | 0.54 ± 0.08 | 0.55 ± 0.12 | 0.54 ± 0.08 | 0.60 ± 0.13 |
Random Undersampling | 0.60 ± 0.12 | 0.63 ± 0.11 | 0.61 ± 0.12 | 0.63 ± 0.11 | 0.67 ± 0.12 |
Random Oversampling | 0.74 ± 0.12 | 0.75 ± 0.09 | 0.76 ± 0.11 | 0.75 ± 0.09 | 0.86 ± 0.08 |
SMOTE | 0.75 ± 0.13 | 0.77 ± 0.09 | 0.78 ± 0.12 | 0.77 ± 0.09 | 0.88 ± 0.06 |
Borderline SMOTE | 0.76 ± 0.14 | 0.77 ± 0.08 | 0.78 ± 0.13 | 0.77 ± 0.08 | 0.88 ± 0.07 |
SMOTE Tomek | 0.73 ± 0.15 | 0.75 ± 0.10 | 0.76 ± 0.13 | 0.75 ± 0.10 | 0.86 ± 0.06 |
SMOTEENN | 0.84 ± 0.12 | 0.85 ± 0.08 | 0.86 ± 0.10 | 0.85 ± 0.08 | 0.92 ± 0.06 |
Our Approach | 0.90 ± 0.03 | 0.90 ± 0.04 | 0.90 ± 0.02 | 0.90 ± 0.04 | 0.95 ± 0.05 |
