Table 2.
Accuracy of deep models without using feature optimization and fusion steps.
| Classifier | Deep Model Features | Evaluation Measures | |||
|---|---|---|---|---|---|
| AlexNet | VGG16 | Accuracy (%) | Error Rate (%) | Time (Seconds) | |
| MC-SVM | ✓ | 94.4 | 5.6 | 39.366 | |
| ✓ | 92.4 | 7.6 | 42.896 | ||
| DT | ✓ | 88.3 | 11.7 | 43.266 | |
| ✓ | 88.7 | 11.3 | 40.246 | ||
| LDA | ✓ | 90.1 | 9.9 | 53.042 | |
| ✓ | 89.6 | 10.4 | 59.160 | ||
| KNB | ✓ | 91.6 | 8.4 | 86.116 | |
| ✓ | 87.5 | 12.5 | 94.204 | ||
| QSVM | ✓ | 92.3 | 7.7 | 45.125 | |
| ✓ | 93.6 | 6.4 | 49.334 | ||
| F-KNN | ✓ | 90.7 | 9.3 | 36.846 | |
| ✓ | 92.4 | 7.6 | 44.116 | ||
| Cosine KNN | ✓ | 91.1 | 8.9 | 42.200 | |
| ✓ | 92.9 | 7.1 | 51.244 | ||
| EBT | ✓ | 90.0 | 10.0 | 60.116 | |
| ✓ | 92.7 | 7.3 | 69.201 | ||