Table 5. Performance evaluation by classification model
Machine learning | Evaluation | Feature selection | PCA | PCA + SMOTE | AE | AE + SMOTE |
Logistic regression | Accuracy | 0.73 | 0.70 | 0.67 | 0.62 | 0.50 |
Precision | 0.77 | 0.73 | 0.68 | 0.68 | 0.51 |
Recall | 0.87 | 0.89 | 0.68 | 0.84 | 0.54 |
F1 score | 0.82 | 0.80 | 0.68 | 0.75 | 0.52 |
SVM | Accuracy | 0.71 | 0.71 | 0.61 | 0.61 | 0.50 |
Precision | 0.75 | 0.73 | 0.60 | 0.68 | 0.51 |
Recall | 0.86 | 0.93 | 0.70 | 0.82 | 0.68 |
F1 score | 0.80 | 0.81 | 0.65 | 0.74 | 0.58 |
Random forest | Accuracy | 0.72 | 0.70 | 0.64 | 0.59 | 0.49 |
Precision | 0.75 | 0.75 | 0.61 | 0.67 | 0.50 |
Recall | 0.90 | 0.85 | 0.81 | 0.76 | 0.73 |
F1 score | 0.82 | 0.79 | 0.70 | 0.72 | 0.60 |
AdaBoost | Accuracy | 0.70 | 0.70 | 0.68 | 0.61 | 0.49 |
Precision | 0.70 | 0.72 | 0.65 | 0.32 | 0.48 |
Recall | 0.98 | 0.94 | 0.72 | 0.19 | 0.43 |
F1 score | 0.82 | 0.81 | 0.69 | 0.24 | 0.46 |
PCA, principal component analysis; SMOTE, synthetic minority oversampling technique; AE, autoencoder; SVM, support vector machine.