Table 8 FS vs. FE for binary classification with 33 features
Models | Accuracy (%) | Precision (%) | Re-call (%) | F1-score (%) | MCC | FS (s) | Training (s) | Inference (ms) |
|---|---|---|---|---|---|---|---|---|
Feature selection | ||||||||
 DT | 86.40 | 84.96 | 86.19 | 85.47 | 0.7114 | 8.28 | 0.64 | 17.69 |
 RF | 85.90 | 84.45 | 86.17 | 85.07 | 0.7059 | 11.74 | 848.32 | |
 kNN | 83.75 | 86.96 | 78.30 | 80.30 | 0.6469 | 0.13 | 231,367.82 | |
 NB | 79.92 | 85.77 | 72.51 | 74.33 | 0.5675 | 0.27 | 40.52 | |
 MLP | 86.45 | 85.01 | 86.29 | 85.54 | 0.7129 | 75.38 | 184.73 | |
Feature extraction | ||||||||
 DT | 86.83 | 85.42 | 86.59 | 85.91 | 0.7201 | 6.13 | 3.13 | 11.58 |
 RF | 86.58 | 85.15 | 86.40 | 85.67 | 0.7154 | 38.67 | 657.02 | |
 kNN | 89.10 | 87.78 | 89.28 | 88.39 | 0.7669 | 0.06 | 227,237.45 | |
 NB | 83.37 | 83.56 | 79.55 | 80.89 | 0.6299 | 0.27 | 45.47 | |
 MLP | 86.54 | 85.11 | 86.35 | 85.62 | 0.7151 | 45.43 | 84.14 | |