Table 10 FS vs. FE for binary classification with 77 (full) features
Models | Accuracy (%) | Precision (%) | Re-call (%) | F1-score (%) | MCC | FS (s) | Training (s) | Inference (ms) |
|---|---|---|---|---|---|---|---|---|
Feature selection | ||||||||
 DT | 78.28 | 76.59 | 75.24 | 75.79 | 0.5128 | 0 | 1.65 | 24.21 |
 RF | 88.22 | 86.99 | 89.56 | 87.69 | 0.7651 | 12.94 | 553.13 | |
 kNN | 80.55 | 80.74 | 83.44 | 80.19 | 0.6413 | 0.09 | 188,417.64 | |
 NB | 59.57 | 71.04 | 67.75 | 59.20 | 0.3865 | 0.36 | 55.02 | |
 MLP | 86.58 | 85.15 | 86.38 | 85.66 | 0.7153 | 70.78 | 83.49 | |
Feature extraction | ||||||||
 DT | 74.68 | 73.37 | 75.10 | 73.64 | 0.4845 | 3.98 | 10.01 | 12.25 |
 RF | 87.04 | 85.65 | 86.78 | 86.14 | 0.7243 | 47.68 | 579.27 | |
 kNN | 80.56 | 80.75 | 83.45 | 80.19 | 0.6414 | 0.08 | 186,251.17 | |
 NB | 79.76 | 81.24 | 73.97 | 75.58 | 0.5473 | 0.28 | 63.89 | |
 MLP | 86.59 | 85.16 | 86.39 | 85.67 | 0.7153 | 86.44 | 152.35 | |