Classification results for each dataset
Dataset | Method | Accuracy | Number of parameters | Number of input nodes removed |
---|---|---|---|---|
Brain | DNNs | 0.979 ± 0.064 | 50,880 | 0 |
Proposed method (linear kernel) | 0.967 ± 0.000 | 2,490 | 0 | |
Proposed method (RBF kernel) | 0.985 ± 0.017 | 22,300 | 0 | |
Proposed sparse method (RBF kernel) | 0.912 ± 0.059 | 1,260 | 4 (20%) | |
BV | DNNs | 0.868 ± 0.030 | 4,304,000 | 0 |
Proposed method (linear kernel) | 0.767 ± 0.020 | 96,900 | 0 | |
Proposed method (RBF kernel) | 0.889 ± 0.025 | 24,470 | 0 | |
Proposed sparse method (RBF kernel) | 0.866 ± 0.025 | 3,670 | 3 (15%) |