Table 8.
Results of machine learning classifiers using CQT features with CTGAN.
| Model | Class | Precision | Recall | F1 Score | Model | Class | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|---|---|---|
| DT | 0 | 0.86 | 0.89 | 0.88 | LR | 0 | 0.99 | 0.99 | 0.99 |
| 1 | 0.99 | 0.98 | 0.99 | 1 | 1.00 | 1.00 | 1.00 | ||
| 2 | 0.90 | 0.89 | 0.89 | 2 | 1.00 | 1.00 | 1.00 | ||
| Micro avg. | 0.92 | 0.92 | 0.92 | Micro avg. | 1.00 | 1.00 | 1.00 | ||
| Weighted avg. | 0.92 | 0.92 | 0.92 | Weighted avg. | 1.00 | 1.00 | 1.00 | ||
| Accuracy | 0.92 | Accuracy | 1.00 | ||||||
| SVC | 0 | 0.99 | 0.99 | 0.99 | NB | 0 | 1.00 | 1.00 | 1.00 |
| 1 | 1.00 | 1.00 | 1.00 | 1 | 1.00 | 1.00 | 1.00 | ||
| 2 | 0.99 | 1.00 | 0.99 | 2 | 1.00 | 1.00 | 1.00 | ||
| Micro avg. | 1.00 | 0.99 | 0.99 | Micro avg. | 1.00 | 1.00 | 1.00 | ||
| Weighted avg. | 1.00 | 0.99 | 0.99 | Weighted avg. | 1.00 | 1.00 | 1.00 | ||
| Accuracy | 1.00 | Accuracy | 1.00 | ||||||
| KNN | 0 | 0.53 | 0.99 | 0.69 | RF | 0 | 1.00 | 1.00 | 1.00 |
| 1 | 1.00 | 0.34 | 0.50 | 1 | 1.00 | 1.00 | 1.00 | ||
| 2 | 0.97 | 0.80 | 0.88 | 2 | 1.00 | 1.00 | 1.00 | ||
| Micro avg. | 0.83 | 0.71 | 0.69 | Micro avg. | 1.00 | 1.00 | 1.00 | ||
| Weighted avg. | 0.84 | 0.71 | 0.69 | Weighted avg. | 1.00 | 1.00 | 1.00 | ||
| Accuracy | 0.71 | Accuracy | 1.00 | ||||||
| HardVoting | 0 | 1.00 | 1.00 | 1.00 | Soft Voting | 0 | 1.00 | 1.00 | 1.00 |
| 1 | 1.00 | 1.00 | 1.00 | 1 | 1.00 | 1.00 | 1.00 | ||
| 2 | 1.00 | 1.00 | 1.00 | 2 | 1.00 | 1.00 | 1.00 | ||
| Micro avg. | 1.00 | 1.00 | 1.00 | Micro avg. | 1.00 | 1.00 | 1.00 | ||
| Weighted avg. | 1.00 | 1.00 | 1.00 | Weighted avg. | 1.00 | 1.00 | 1.00 | ||
| Accuracy | 1.00 | Accuracy | 1.00 | ||||||