Research Article
A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method
Table 5
Results for the FsBoost.V1 ensemble algorithm (Levels 1 and 2).
| Level | Level 1 | Level 2 | Classifier | kNN ensemble | PNN ensemble | SVM ensemble | Ensemble | Class | Sen | Spe | Acc | Sen | Spe | Acc | Sen | Spe | Acc | Sen | Spe | Acc |
| For A dataset | E | 0.88 | 1.00 | 93.87 | 0.89 | 1.00 | 94.26 | 0.99 | 1.00 | 99.48 | 0.93 | 1.00 | 96.43 | N-E | 1.00 | 0.88 | 1.00 | 0.89 | 1.00 | 0.99 | 1.00 | 0.93 | AUC | 0.94 | 0.94 | 0.99 | 0.96 | Kappa | 0.88 | 0.89 | 0.99 | 0.93 | F-measure | 0.93 | 0.94 | 0.99 | 0.96 |
| For B dataset | E | 0.85 | 1.00 | 92.48 | 0.76 | 1.00 | 87.83 | 0.96 | 0.99 | 97.48 | 0.87 | 1.00 | 93.52 | N-E | 1.00 | 0.85 | 1.00 | 0.76 | 0.99 | 0.96 | ā | 1.00 | 0.87 | AUC | 0.92 | 0.88 | 0.97 | 0.94 | Kappa | 0.85 | 0.76 | 0.95 | 0.87 | F-measure | 0.92 | 0.86 | 0.97 | 0.93 |
| For C dataset | E | 0.84 | 0.99 | 91.91 | 0.80 | 0.99 | 89.17 | 0.97 | 0.98 | 97.43 | 0.88 | 0.99 | 93.78 | N-E | 0.99 | 0.84 | 0.99 | 0.80 | 0.98 | 0.97 | 0.99 | 0.88 | AUC | 0.92 | 0.89 | 0.97 | 0.94 | Kappa | 0.84 | 0.78 | 0.95 | 0.88 | F-measure | 0.91 | 0.88 | 0.97 | 0.93 |
| For D dataset | E | 0.83 | 0.97 | 89.87 | 0.64 | 0.96 | 80.13 | 0.95 | 0.93 | 94.04 | 0.86 | 0.96 | 91.30 | N-E | 0.97 | 0.83 | 0.96 | 0.64 | 0.93 | 0.95 | 0.96 | 0.86 | AUC | 0.90 | 0.80 | 0.94 | 0.91 | Kappa | 0.80 | 0.60 | 0.88 | 0.83 | F-measure | 0.89 | 0.77 | 0.94 | 0.91 |
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Sen: sensitivity, Spe: specificity, Acc: accuracy (%), E: epilepsy, and N-E: nonepilepsy.
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