Classifier | Performance measure | FCBF | Random forest (766) | Random forest (180) | Ensemble SVM-RFE | Our method |
---|---|---|---|---|---|---|
AdaBoost | Accuracy | 0.789 | 0.845 | 0.859 | 0.887 | 0.901 |
F1-score | 0.545 | 0.593 | 0.667 | 0.75 | 0.774 | |
MCC | 0.408 | 0.532 | 0.588 | 0.68 | 0.717 | |
AUC | 0.7 | 0.717 | 0.766 | 0.825 | 0.834 | |
Logistic regression | Accuracy | 0.789 | 0.789 | 0.817 | 0.845 | 0.845 |
F1-score | 0.651 | 0.595 | 0.667 | 0.718 | 0.732 | |
MCC | 0.533 | 0.456 | 0.552 | 0.623 | 0.646 | |
AUC | 0.801 | 0.74 | 0.799 | 0.838 | 0.858 | |
Random forest | Accuracy | 0.817 | 0.817 | 0.803 | 0.831 | 0.845 |
F1-score | 0.48 | 0.48 | 0.462 | 0.5 | 0.56 | |
MCC | 0.426 | 0.426 | 0.381 | 0.479 | 0.531 | |
AUC | 0.658 | 0.658 | 0.649 | 0.667 | 0.697 | |
SVM | Accuracy | 0.803 | 0.831 | 0.859 | 0.831 | 0.901 |
F1-score | 0.632 | 0.571 | 0.583 | 0.684 | 0.811 | |
MCC | 0.504 | 0.489 | 0.589 | 0.577 | 0.749 | |
AUC | 0.77 | 0.708 | 0.706 | 0.808 | 0.895 |