Table 10.
Performance comparison of the classifier, for the Basic AE, when changing the imputation strategy at the data preprocessing step
| Strategy | Top Layers (AEs) | Accuracy (%) | MCC | Precision (%) | Recall (%) | F1 score |
|---|---|---|---|---|---|---|
| Fixing the AE weights (Approach A) | ||||||
| Mean ∗ | AE: Encoding Layers | 88.40 ±5.52 | 0.59 ±0.17 | 68.39 ±19.13 | 64.80 ±10.84 | 65.91 ±13.72 |
| AE: Complete AE | 91.77 ±3.13 | 0.69 ±0.12 | 80.57 ±11.79 | 67.00 ±11.24 | 72.91 ±10.86 | |
| CV | AE: Encoding Layers | 91.93 ±2.13 | 0.69 ±0.10 | 79.43 ±6.20 | 69.40 ±10.96 | 73.81 ±8.14 |
| AE: Complete AE | 93.23 ±1.99 | 0.74 ±0.08 | 83.41 ±5.85 | 74.20 ±9.59 | 78.31 ±6.95 | |
| MFV | AE: Encoding Layers | 92.50 ±2.36 | 0.71 ±0.10 | 82.60 ±7.41 | 70.00 ±13.40 | 75.16 ±9.23 |
| AE: Complete AE | 93.27 ±1.71 | 0.74 ±0.07 | 84.97 ±4.01 | 72.40 ±9.74 | 77.91 ±6.54 | |
| Fine-Tuning the AE Weights (Approach B) | ||||||
| Mean ∗ | AE: Encoding Layers | 99.33 ±0.52 | 0.98 ±0.02 | 97.85 ±2.32 | 98.20 ±1.48 | 98.01 ±1.55 |
| AE: Complete AE | 99.30 ±0.37 | 0.98 ±0.01 | 99.00 ±1.06 | 96.80 ±2.35 | 97.87 ±1.15 | |
| CV | AE: Encoding Layers | 99.40 ±0.49 | 0.98 ±0.02 | 98.63 ±2.04 | 97.80 ±2.39 | 98.23 ±1.48 |
| AE: Complete AE | 99.30 ±0.53 | 0.98 ±0.02 | 99.01 ±1.39 | 96.80 ±3.29 | 97.97 ±1.38 | |
| MFV | AE: Encoding Layers | 99.47 ±0.32 | 0.98 ±0.01 | 98.83 ±1.64 | 98.00 ±2.11 | 98.39 ±0.98 |
| AE: Complete AE | 99.13 ±0.57 | 0.97 ±0.02 | 98.77 ±1.71 | 96.00 ±2.31 | 97.36 ±1.74 |
The experiment pipeline remains the same, under the same evaluation metrics. The Strategy column represents the imputation strategy used. The ∗ symbol represents the default strategy. The following abreviations were used: CV for Constante Value, and MFV for Most Frequent Value