Table 4 Results of the proposed IBAVO-AO algorithm and its components for FND
From: An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection
Metric | IBAVO-AO | BAVO | BAO |
|---|---|---|---|
Mean (Accuracy) | 0.9275 | 0.9262 | 0.9261 |
SD (Accuracy) | 0.0006 | 0.0008 | 0.0008 |
Mean (Fitness) | 0.0753 | 0.0773 | 0.0773 |
SD (Fitness) | 0.0006 | 0.0009 | 0.0008 |
Mean (Features size) | 17.5000 | 21.1600 | 20.5300 |
SD (Features size) | 02.1211 | 02.5701 | 01.9619 |
Mean (Kappa) | 0.8555 | 0.8528 | 0.8527 |
SD (Kappa) | 0.0011 | 0.0016 | 0.0015 |
Mean (Precision) | 0.9843 | 0.9834 | 0.9834 |
SD (Precision) | 0.0014 | 0.0013 | 0.0015 |
Mean (Recall) | 0.8771 | 0.8753 | 0.8752 |
SD (Recall) | 0.0015 | 0.0017 | 0.0016 |
Mean (F1-score) | 0.9276 | 0.9262 | 0.9262 |
SD (F1-score) | 0.0006 | 0.0008 | 0.0008 |
Mean (Specificity) | 0.9843 | 0.9834 | 0.9834 |
SD (Specificity) | 0.0015 | 0.0017 | 0.0016 |
Mean (Sensitivity) | 0.8771 | 0.8753 | 0.8752 |
SD (Sensitivity) | 0.0015 | 0.0017 | 0.0016 |
Mean (ROC_AUC) | 0.9657 | 0.9654 | 0.9656 |
SD (ROC_AUC) | 0.0009 | 0.0010 | 0.0011 |
Mean (MCC) | 0.8611 | 0.8587 | 0.8585 |
SD (MCC) | 0.0012 | 0.0019 | 0.0015 |