Table 4 Comparison of the performance of Support Vector Machine (SVM) classifier with sampling using polynomial, normalized polynomial and puk kernels using complexity parameters 0.1, 10 and 30
| Â | Polynomial | Normalized Polynomial | Puk | ||||||
|---|---|---|---|---|---|---|---|---|---|
C = 0.1 | C = 10 | C = 30 | C = 0.1 | C = 10 | C = 30 | C = 0.1 | C = 10 | C = 30 | |
Sensitivity | 36.18% | 36.18% | 36.18% | 100% | 95.10% | 65.10% | 47.38% | 81.94% | 80.26% |
Specificity | 94.37% | 94.37% | 94.37% | 88.31% | 88.79% | 88.85% | 88.58% | 94.13% | 95.19% |
Precision | 61.46% | 61.41% | 61.41% | 0.02% | 33.67% | 5.62% | 6.33% | 53.64% | 62.63% |
F-score | 45.55% | 45.53% | 45.53% | 0.05% | 49.73% | 10.35% | 11.17% | 64.84% | 70.36% |
RMSE | 0.41 | 0.42 | 0.42 | 0.34 | 0.34 | 0.34 | 0.35 | 0.26 | 0.25 |
AUC | 0.74 | 0.74 | 0.74 | 0.5 | 0.52 | 0.53 | 0.53 | 0.76 | 0.8 |