Table 12 Comparison of the accuracy for the best found ELM and MLP solutions for given classification datasets
From: Extreme learning machine versus classical feedforward network
Dataset | Model | Accuracy (%) |
|---|---|---|
Breast cancer wisconsin) | ELM (own) | 93.02 |
MLP [48] | 96.30 | |
Breast cancer (Ljubljana) | ELM (own) | 71.43 |
MLP [48] | 73.50 | |
Wine | ELM (own) | 96.30 |
MLP [48] | 98.30 | |
Urban land cover | ELM (own) | 87.25 |
MLP (own) | 83.33 | |
Sonar | ELM (own) | 84.38 |
MLP [48] | 76.40 | |
Glass | ELM (own) | 54.55 |
MLP [48] | 68.70 | |
Landsat | ELM (own) | 89.23 |
MLP [44] | 91.00 | |
CNAE-9 | ELM (own) | 98.15 |
CoPACoRSET-MLP [35] | 95.95 | |
Forest cover type | ELM (own) | 77.75 |
MLP [27] | 84.78 |