Table 2.
Performance of the compared feature sets on datasets with different positive-to-negative ratios
| Feature | Acc. (%) | Fm.1 (%) | Prec. (%) | Sens. (%) | Spec. (%) |
|---|---|---|---|---|---|
| Datasets with 1:1 positive-to-negative ratio | |||||
| Shen et al.2 | 77.1 ± 0.8 | 77.9 ± 0.8 | 75.2 ± 0.9 | 80.9 ± 1.4 | 73.3 ± 1.4 |
| Guo et al.3 | 77.2 ± 0.9 | 77.6 ± 0.9 | 76.2 ± 1.0 | 79.1 ± 1.3 | 75.4 ± 1.4 |
| This work4 | 80.1 ± 0.8 | 80.4 ± 0.8 | 79.4 ± 1.0 | 81.4 ± 1.4 | 78.8 ± 1.4 |
| Datasets with 1:3 positive-to-negative ratio | |||||
| Shen et al. | 82.2 ± 0.3 | 58.6 ± 1.1 | 69.9 ± 0.8 | 50.4 ± 1.6 | 92.7 ± 0.3 |
| Guo et al. | 82.1 ± 0.6 | 58.3 ± 1.7 | 69.8 ± 1.6 | 50.1 ± 1.8 | 92.8 ± 0.4 |
| This work | 83.6 ± 0.5 | 66.7 ± 1.2 | 67.9 ± 0.9 | 65.5 ± 1.7 | 89.7 ± 0.4 |
| Datasets with 1:7 positive-to-negative ratio | |||||
| Shen et al. | 88.0 ± 0.3 | 45.4 ± 1.7 | 52.8 ± 1.8 | 39.9 ± 1.9 | 94.9 ± 0.3 |
| Guo et al. | 87.2 ± 0.3 | 45.5 ± 1.3 | 48.8 ± 1.5 | 42.6 ± 1.3 | 93.6 ± 0.3 |
| This work | 90.6 ± 0.2 | 52.8 ± 1.7 | 71.5 ± 1.5 | 41.8 ± 1.8 | 97.6 ± 0.2 |
| Datasets with 1:15 positive-to-negative ratio | |||||
| Shen et al. | 92.5 ± 0.1 | 33.1 ± 1.4 | 37.5 ± 1.3 | 29.7 ± 1.5 | 96.7 ± 0.1 |
| Guo et al. | 91.7 ± 0.2 | 36.6 ± 1.5 | 35.1 ± 1.5 | 38.3 ± 1.9 | 95.3 ± 0.2 |
| This work | 93.7 ± 0.2 | 43.6 ± 1.3 | 49.5 ± 1.7 | 39.0 ± 1.3 | 97.3 ± 0.1 |
The best performance among each positive-to-negative ratio is highlighted with bold font. 1The parameter selection is based on a five-fold cross validation of the training dataset to maximize the F-measure. 2Using triad frequency as the feature set. 3Using auto cross covariance as the feature set. 4Using triad significance as the feature set.