Research Article
A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method
Table 4
Level 1 classification results for the A dataset.
| k-nearest neighbor classification algorithm |
| NP | k = 2 | k = 2 | k = 2 | DF | Euclidean | Seuclidean | Euclidean | FS | 0 | 1 | 2 | NF | 68 | 25 | 15 | Class | Sen | Spe | Acc | Sen | Spe | Acc | Sen | Spe | Acc | E | 0.84 | 1.00 | 92.17 | 0.87 | 0.89 | 93.26 | 0.89 | 1.00 | 94.57 | N-E | 1.00 | 0.84 | 1.00 | 1.00 | 1.00 | 0.89 | AUC | 0.92 | 0.87 | 0.94 | Kappa | 0.84 | 0.93 | 0.88 | F-measure | 0.92 | 93.26 | 0.93 | 10-fold (%) | 88.85 | 91.22 | 92.54 |
| Probabilistic neural networks |
| NP | Spread | Spread | Spread | 0.11 | 0.11 | 0.21 | FS | 0 | 1 | 2 | NF | 68 | 25 | 15 | Class | Sen | Spe | Acc | Sen | Spe | Acc | Sen | Spe | Acc | E | 0.87 | 1.00 | 93.26 | 0.94 | 0.75 | 93.09 | 0.75 | 1.00 | 87.70 | N-E | 1.00 | 0.87 | 0.92 | 1.00 | 1.00 | 0.75 | AUC | 0.93 | 0.86 | 0.94 | Kappa | 0.87 | 0.93 | 0.89 | F-measure | 0.93 | 93.09 | 0.94 | 10-fold (%) | 0.11 | 0.11 | 0.21 |
| Support vector machines |
| NP | BoxConstraint | 3 | 21 | 2 | FS | 0 | 1 | 2 | NF | 68 | 25 | 15 | Class | Sen | Spe | Acc | Sen | Spe | Acc | Sen | Spe | Acc | E | 0.99 | 1.00 | 99.65 | 0.99 | 0.98 | 99.39 | 0.98 | 0.99 | 98.83 | N-E | 1.00 | 0.99 | 1.00 | 0.99 | 0.99 | 0.98 | AUC | 1.00 | 0.99 | 0.99 | Kappa | 0.99 | 0.99 | 0.99 | F-measure | 1.00 | 99.39 | 0.99 | 10-fold (%) | 99.76 | 99.54 | 99.02 |
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DF: distance function, Sen: sensitivity, Spe: specificity, Acc: accuracy (%), NP: network parameters, FS: feature selection, NF: number of features, EC: ensemble classifier, E: epilepsy, and N-E: nonepilepsy.
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