Table 6.
The AUCs of SVM-based Classifiers with Different Kernel Functions by Leave-one-out Procedures Using Different Feature Subsets
| Feature num. in a subset | AUCs of SVM with Gaussian | AUCs of SVM with linear | AUCs of SVM with polycon (power = 2) | AUCs of SVM with polycon (power = 3) |
|---|---|---|---|---|
| 5 | 0.825581395 | 0.870725034 | 0.856361149 | 0.893296854 |
| 10 | 0.859097127 | 0.863885089 | 0.834473324 | 0.892612859 |
| 15 | 0.856361149 | 0.831737346 | 0.744186047 | 0.865253078 |
| 20 | 0.868673051 | 0.831053352 | 0.800273598 | 0.881668947 |
| 25 | 0.807797538 | 0.765389877 | 0.794801642 | 0.79753762 |
| 30 | 0.819425445 | 0.883036936 | 0.764021888 | 0.763337893 |
| Average | 0.830106282 | 0.844286015 | 0.801720509 | 0.832789645 |