Table 4.
Accuracy results under multi-class classification.
| Dataset | LSVM | BN | NB-K | KNN | AdaBoostM1 | Bagging | DT | RF |
|---|---|---|---|---|---|---|---|---|
| Sub-dataset 1 | 27.51% | 71.65% | 16.17% | 86.25% | 25.29% | 79.56% | 9.01% | 79.21% |
| Sub-dataset 3 | 28.99% | 73.11% | 18.76% | 87.66% | 22.53% | 77.40% | 8.46% | 77.51% |
| Sub-dataset 5 | 27.17% | 74.64% | 19.71% | 87.04% | 21.00% | 79.15% | 9.01% | 78.78% |
| Sub-dataset 7 | 29.79% | 73.70% | 14.32% | 86.90% | 20.61% | 78.61% | 74.57% | 77.85% |
| Sub-dataset 9 | 30.36% | 70.21% | 18.61% | 85.86% | 25.02% | 74.23% | 25.60% | 74.08% |
| Sub-dataset 10 | 23.56% | 73.30% | 17.01% | 87.59% | 19.38% | 79.24% | 24.74% | 78.54% |
| Sub-dataset 13 | 27.43% | 72.89% | 13.79% | 87.00% | 19.35% | 75.79% | 82.60% | 78.62% |
| Sub-dataset 15 | 27.54% | 73.88% | 19.45% | 87.06% | 29.44% | 72.37% | 83.19% | 73.29% |