Table 2 Compression of imbalanced data handling techniques using accuracy and AUC values.
Algorithms | Comparison method | Unbalanced data | Under-sampling | Over-sampling | ADASYN | SMOTE |
|---|---|---|---|---|---|---|
SVM | Accuracy (%) | 65.0% | 69.0% | 74.0% | 66.0% | 72.0% |
AUC | 0.508 | 0.600 | 0.529 | 0.513 | 0.491 | |
Gaussian naive baye | Accuracy (%) | 59.0% | 60.0% | 67.0% | 59.0% | 68.0% |
AUC | 0.660 | 0.668 | 0.691 | 0.632 | 0.669 | |
Logistic regression | Accuracy (%) | 64.0% | 71.0% | 73.0% | 60.0% | 72.0% |
AUC | 0.684 | 0.703 | 0.703 | 0.657 | 0.698 | |
Decision tree classifier | Accuracy (%) | 56.0% | 65.0% | 70.0% | 77.0% | 88.0% |
AUC | 0.467 | 0.504 | 0.467 | 0.825 | 0.829 | |
Random forest classifier | Accuracy (%) | 60.0% | 71.0% | 76.0% | 88.0% | 91.0% |
AUC | 0.635 | 0.656 | 0.635 | 0.853 | 0.878 | |
Gradient boosting classifier | Accuracy (%) | 67.0% | 76.0% | 73.0% | 87.0% | 87.0% |
AUC | 0.639 | 0.636 | 0.639 | 0.728 | 0.748 | |
XGBoost | Accuracy (%) | 66.0% | 69.0% | 73.0% | 76.0% | 78.0% |
AUC | 0.565 | 0.594 | 0.577 | 0.626 | 0.637 | |
KNN | Accuracy (%) | 61.0% | 68.0% | 71.0% | 69.0% | 88.0% |
AUC | 0.606 | 0.632 | 0.613 | 0.784 | 0.815 |
