Table 3 Performance comparison of the three-feature SVM classifier to linear classifiers, an RBF network classifier and other SVM classifiers, using canonical training and testing datasets.
From: IsoSVM – Distinguishing isoforms and paralogs on the protein level
Accuracy | |||
|---|---|---|---|
Feature(s) | Canonical testing dataset | Homologous-regions-only testing dataset | |
3-feature SVM classifier | Sequence similarity, inverse CBIN count, match/mismatch fraction (cf. Table 2) | 99.63% | 98.98% |
2-feature SVM classifiers | Match/mismatch fraction, sequence similarity | 97.50% | 96.68% |
Inverse CBIN count, sequence similarity | 99.32% | 98.97% | |
Match/mismatch fraction, inverse CBIN count | 99.42% | 98.91% | |
RBF Network classifier | Sequence similarity, inverse CBIN count, match/mismatch fraction | 99.32% | 98.79% |
3-feature linear classifier | Sequence similarity, inverse CBIN count, match/mismatch fraction | 99.42% | 98.80% |
2-feature linear classifiers | Match/mismatch fraction, sequence similarity | 99.03% | 98.75% |
Inverse CBIN count, sequence similarity | 99.32% | 98.67% | |
Match/mismatch fraction, inverse CBIN count | 99.37% | 98.77% | |
1-feature linear classifiers | Sequence similarity | 82.22% | 82.02% |
Match/mismatch fraction | 98.05% | 98.62% | |
Inverse CBIN count | 99.37% | 98.75% | |