Research Article
SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine
Table 2
The comparison results between SGB-ELM and other representative algorithms on 4 regression datasets.
| Dataset | Algorithm | Training time | Training RMSE (Dev) | Testing RMSE (Dev) | Hidden nodes | Iterations |
| Laser () | ELM | 0.0097 | 12.3791 0.6067 | 12.7783 1.5863 | 80 | N/A | Simple ensemble | 0.1547 | 12.1216 0.4585 | 12.8794 1.5415 | 80 | 10 | Bagging | 0.7991 | 12.1707 0.5094 | 13.4085 1.5998 | 80 | 50 | Adaboost | 0.1591 | 11.4460 0.4875 | 12.0666 1.2080 | 50 | Max = 50 | SGB-ELM | 3.4853 | 7.6354 0.3919 | 8.4170 1.0788 | 50 () | 50 |
| Friedman () | ELM | 0.0141 | 1.4220 0.0532 | 1.5124 0.0906 | 100 | N/A | Simple ensemble | 0.2081 | 1.4005 0.0240 | 1.4791 0.0747 | 100 | 10 | Bagging | 1.0144 | 1.4111 0.0197 | 1.4906 0.0701 | 100 | 50 | Adaboost | 0.5219 | 1.2551 0.0304 | 1.3342 0.0587 | 60 | Max = 50 | SGB-ELM | 4.8853 | 1.0627 0.0136 | 1.1581 0.0346 | 60 () | 50 |
| Mortgage () | ELM | 0.0200 | 0.0855 0.0022 | 0.0961 0.0078 | 150 | N/A | Simple ensemble | 0.3044 | 0.0843 0.0021 | 0.0947 0.0085 | 150 | 10 | Bagging | 1.4544 | 0.0834 0.0018 | 0.0937 0.0077 | 150 | 50 | Adaboost | 0.4778 | 0.0785 0.0020 | 0.0885 0.0058 | 80 | Max = 50 | SGB-ELM | 6.2434 | 0.0607 0.0015 | 0.0759 0.0056 | 80 () | 50 |
| Wizmir () | ELM | 0.0128 | 1.0906 0.0277 | 1.1263 0.0667 | 100 | N/A | Simple ensemble | 0.2066 | 1.0869 0.0269 | 1.1203 0.0629 | 100 | 10 | Bagging | 1.0366 | 1.0859 0.0259 | 1.1165 0.0620 | 100 | 50 | Adaboost | 0.4331 | 1.0622 0.0519 | 1.1091 0.0857 | 60 | Max = 50 | SGB-ELM | 5.6525 | 1.0148 0.0258 | 1.1032 0.0615 | 60 () | 50 |
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