Table 9 FS vs. FE for binary classification with 47 features
Models | Accuracy (%) | Precision (%) | Re-call (%) | F1-score (%) | MCC | FS (s) | Training (s) | Inference (ms) |
|---|---|---|---|---|---|---|---|---|
Feature selection | ||||||||
 DT | 84.23 | 83.44 | 81.68 | 82.40 | 0.6509 | 5.47 | 0.86 | 30.00 |
 RF | 86.23 | 84.82 | 85.76 | 85.23 | 0.7057 | 15.96 | 524.71 | |
 kNN | 82.82 | 82.28 | 79.54 | 80.55 | 0.6176 | 0.09 | 148,293.32 | |
 NB | 81.20 | 84.15 | 75.15 | 77.02 | 0.5861 | 0.28 | 44.95 | |
 MLP | 86.52 | 85.09 | 86.34 | 85.61 | 0.7142 | 67.58 | 72.34 | |
Feature extraction | ||||||||
 DT | 83.81 | 82.92 | 85.61 | 83.26 | 0.6848 | 5.01 | 6.50 | 10.47 |
 RF | 86.94 | 85.54 | 86.72 | 86.04 | 0.7225 | 35.72 | 569.59 | |
 kNN | 86.76 | 85.34 | 87.16 | 86.00 | 0.7129 | 0.05 | 147,798.15 | |
 NB | 69.74 | 70.52 | 59.96 | 58.85 | 0.2859 | 0.21 | 43.87 | |
 MLP | 86.59 | 85.16 | 86.39 | 85.67 | 0.7152 | 59.03 | 105.07 | |