Table 3.
Results were obtained from different algorithms
| Training method | MLP training algorithms | Benchmark dataset | Experimental dataset | ||||
|---|---|---|---|---|---|---|---|
| MSE(AVE±STD) | P-value | Classification rate % | MSE(AVE ± STD) | P-value | Classification rate % | ||
| ChOA | 0.1381 ± 0.1483 | 1.351 | 65.6015 | 0.1388 ± 0.1563 | 2.009 | 35.8461 | |
| Metahsoristic | FChOA | 0.1016 ± 0.1121 | 0.038 | 89.3480 | 0.1006 ± 0.1222 | 0.019 | 89.1355 |
| CVOA | 0.1277 ± 0.1371 | 0.184 | 81.9422 | 0.1254 ± 0.1451 | 0.106 | 79.0043 | |
| BWO | 0.1421 ± 0.1509 | 2.169 | 58.0248 | 0.1254 ± 0.1451 | 2.894 | 58.8453 | |
| HHO | 0.1109 ± 0.1295 | 0.086 | 76.1208 | 0.1192 ± 0.1386 | 0.057 | 74.0199 | |
| KF | 0.1611 ± 0.1662 | 2.871 | 18.01533 | 0.1530 ± 0.17244 | 3.322 | 57.9411 | |
| Traditional | GD | 0.5944 ± 0.6231 | 6.7892 | 13.4590 | 0.69908 ± 0.2576 | 6.908 | 11.9184 |