Classifiers | Precision | TPR/recall/sensitivity | specificity/TNR | accuracy | F-measure | AUC |
---|---|---|---|---|---|---|
Dd3937 | ||||||
Random Forest | 0.93 | 0.81 | 0.98 | 0.94 | 0.87 | 0.97 |
Bayesian Network | 0.91 | 0.85 | 0.97 | 0.94 | 0.88 | 0.97 |
SMO using RBF kernels | 0.93 | 0.85 | 0.98 | 0.95 | 0.89 | 0.92 |
SMO using polynormial kernels | 0.91 | 0.87 | 0.97 | 0.95 | 0.95 | 0.89 |
Adaptive Boosting (Naïve Bayes)* | 0.84 | 0.89 | 0.95 | 0.93 | 0.87 | 0.96 |
Adaptive Boosting (Decision Tree)* | 0.96 | 0.91 | 0.99 | 0.97 | 0.93 | 0.98 |
Adaptive Boosting (IBK)* | 0.96 | 0.84 | 0.99 | 0.95 | 0.90 | 0.99 |
Adaptive Boosting (Decision Stump)* | 0.92 | 0.87 | 0.98 | 0.95 | 0.89 | 0.97 |
Multi-Boosting (Decision Tree)* | 0.97 | 0.91 | 0.99 | 0.97 | 0.94 | 0.98 |
Multi-Boosting (IBK)* | 0.91 | 0.77 | 0.98 | 0.93 | 0.84 | 0.93 |
Multi-Boosting (Naïve Bayes)* | 0.90 | 0.91 | 0.97 | 0.95 | 0.91 | 0.96 |
Logit-Boosting (Decision Stump)* | 0.91 | 0.90 | 0.97 | 0.96 | 0.91 | 0.98 |
WPP14 | ||||||
Random Forest | 0.89 | 0.81 | 0.97 | 0.93 | 0.85 | 0.97 |
Bayesian Network | 0.90 | 0.83 | 0.97 | 0.94 | 0.87 | 0.97 |
SMO using RBF kernels | 0.94 | 0.84 | 0.98 | 0.95 | 0.89 | 0.91 |
SMO using polynormial kernels | 0.93 | 0.86 | 0.98 | 0.95 | 0.95 | 0.89 |
Adaptive Boosting (Naïve Bayes)* | 0.89 | 0.89 | 0.97 | 0.95 | 0.89 | 0.96 |
Adaptive Boosting (Decision Tree)* | 0.95 | 0.86 | 0.99 | 0.96 | 0.90 | 0.98 |
Adaptive Boosting (IBK)* | 0.87 | 0.83 | 0.96 | 0.93 | 0.85 | 0.92 |
Logit-Boosting (Decision Stump)* | 0.90 | 0.85 | 0.97 | 0.94 | 0.88 | 0.97 |
Multi-Boosting (Decision Tree)* | 0.94 | 0.86 | 0.98 | 0.96 | 0.90 | 0.98 |
Multi-Boosting (Decision Stump)* | 0.91 | 0.75 | 0.98 | 0.93 | 0.82 | 0.97 |
Multi-Boosting (Naïve Bayes)* | 0.90 | 0.89 | 0.97 | 0.95 | 0.89 | 0.96 |
Logit-Boosting (Decision Stump)* | 0.90 | 0.87 | 0.97 | 0.95 | 0.89 | 0.97 |