Table 9 The results of the transformer-based embedding features with different ML classifiers.
From: A hybrid super learner ensemble for phishing detection on mobile devices
Metrics | ML1 | ML2 | ML3 | ML4 | ML5 |
|---|---|---|---|---|---|
Recall (%) | 94.51 | 95.32 | 96.96 | 94.56 | 93.16 |
TNR (%) | 92.82 | 93.88 | 93.35 | 92.86 | 91.23 |
Precision (%) | 94.69 | 95.47 | 94.93 | 94.72 | 93.53 |
F1-Score | 94.60 | 95.39 | 95.93 | 94.64 | 93.34 |
Accuracy (%) | 93.80 | 94.71 | 95.38 | 93.84 | 92.34 |
MCC (%) | 87.31 | 89.18 | 90.61 | 87.40 | 84.33 |
