Research Article
Fake News Detection Using Machine Learning Ensemble Methods
Table 4
Recall on the 4 datasets.
| ā | DS1 | DS2 | DS3 | DS4 |
| Logistic regression (LR) | 0.98 | 0.9 | 0.92 | 0.86 | Linear SVM (LSVM) | 0.98 | 0.32 | 1 | 0.86 | Multilayer perceptron | 1 | 0.36 | 0.96 | 0.88 | K-nearest neighbors (KNN) | 0.87 | 0.24 | 0.81 | 0.74 |
| Ensemble learners | Random forest (RF) | 1 | 0.34 | 0.93 | 0.91 | Voting classifier (RF, LR, KNN) | 0.97 | 0.89 | 0.96 | 0.9 | Voting classifier (LR, LSVM, CART) | 0.97 | 0.87 | 0.96 | 0.89 | Bagging classifier (decision trees) | 0.97 | 0.95 | 0.94 | 0.91 | Boosting classifier (AdaBoost) | 0.98 | 0.93 | 0.92 | 0.86 | Boosting classifier (XGBoost) | 0.99 | 0.94 | 0.94 | 0.89 |
| Benchmark algorithms | Perez-LSVM | 0.99 | 0.81 | 0.97 | 0.91 | Wang-CNN | 0.9 | 0.71 | 0.29 | 0.75 | Wang-Bi-LSTM | 0.78 | 0.59 | 0.35 | 0.61 |
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