Table 3 Proposed features fusion and selection results on PH2 dataset
Method | Execution time /sec | Sensitivity (%) | Precision (%) | Specificity (%) | FNR (%) | FPR | Accuracy (%) |
|---|---|---|---|---|---|---|---|
DT | 7 | 88.33 | 88.73 | 92.50 | 10.0 | 0.04 | 90.0 |
QDA | 2 | 90.83 | 89.40 | 91.20 | 9.0 | 0.04 | 91.0 |
Q-SVM | 2 | 95.83 | 96.60 | 98.70 | 3.0 | 0.01 | 97.0 |
LR | 6 | 92.10 | 92.76 | 96.96 | 6.0 | 0.02 | 94.0 |
N-B | 3 | 89.60 | 91.73 | 96.90 | 7.5 | 0.03 | 92.5 |
W-KNN | 2 | 91.67 | 92.33 | 96.20 | 6.5 | 0.02 | 93.5 |
EBT | 5 | 95.43 | 96.67 | 98.12 | 3.5 | 0.02 | 96.5 |
ESD | 10 | 94.20 | 94.53 | 97.50 | 4.5 | 0.02 | 95.5 |
C-KNN | 2 | 91.26 | 91.56 | 95.61 | 7.0 | 0.03 | 93.0 |
Multi-class SVM | 1 | 96.67 | 97.06 | 98.74 | 2.5 | 0.01 | 97.5 |