Table 3 Comparison of classification performance of various methods.
From: Spammer detection using multi-classifier information fusion based on evidential reasoning rule
Methods | Accuracy | Precision | Recall | F1-score |
|---|---|---|---|---|
SV | 84.48% | 88.32% | 81.76% | 0.8493 |
WSV | 85.12% | 90.51% | 81.58% | 0.8582 |
DS | 85.45% | 89.05% | 82.99% | 0.8589 |
ERA | 86.18% | 87.22% | 84.67% | 0.8593 |
Bagging | 85.82% | 85.40% | 86.03% | 0.8571 |
AdaBoost | 87.27% | 89.05% | 85.92% | 0.8737 |
Random forest | 86.55% | 84.67% | 87.88% | 0.8625 |
GNB | 84.36% | 85.07% | 83.21% | 0.8413 |
XGBoost | 86.91% | 85.82% | 87.12% | 0.8647 |
MICFA | 87.63% | 87.05% | 88.32% | 0.8768 |
SDMER | 88.73% | 87.59% | 89.55% | 0.8856 |
