Table 4.
Accuracy of the resulting dataset after having applied of the feature selection methods using the SVM, Boosting (Adaboost) and Naive Bayes classifiers
| Accuracy | Precision | Recall | F1score | |
|---|---|---|---|---|
| SVM | ||||
| Gradient Tree Boosting | 98% | 100% | 94% | 96% |
| L1-based | 88% | 86% | 85% | 84% |
| Random Forest | 91% | 91% | 85% | 87% |
| Univariate | 78% | 80% | 78% | 78% |
| Boosting (Adaboost) | ||||
| Gradient Tree Boosting | 94% | 100% | 92% | 9% |
| L1-based | 91% | 91% | 91% | 89% |
| Random Forest | 95% | 100% | 94% | 96% |
| Univariate | 87% | 87% | 87% | 85% |
| NaiveBayes | ||||
| Gradient Tree Boosting | 98% | 96% | 100% | 98% |
| L1-based | 61% | 60% | 78% | 67% |
| Random Forest | 92% | 86% | 98% | 91% |
| Univariate | 60% | 53% | 87% | 66% |