Research Article
A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine
Table 4
Result comparisons of testing RMSE of RAE-ELM and other learning algorithms for real-world data regression problems.
| Datasets | RAE-ELM | Basic ELM [4] | Original AdaELM [30] | Modified AdaELM [31] | SVR [36] | LSSVR [37] |
| LVST | 0.2571 | 0.2854 | 0.2702 | 0.2653 | 0.2849 | 0.2801 | Yeast | 0.1071 | 0.1224 | 0.1195 | 0.1156 | 0.1238 | 0.1182 | Computer hardware | 0.0563 | 0.0839 | 0.0821 | 0.0726 | 0.0976 | 0.0771 | Abalone | 0.0731 | 0.0882 | 0.0793 | 0.0778 | 0.0890 | 0.0815 | Servo | 0.0727 | 0.0924 | 0.0884 | 0.0839 | 0.0966 | 0.0870 | Parkinson disease | 0.2219 | 0.2549 | 0.2513 | 0.2383 | 0.2547 | 0.2437 | Housing | 0.1018 | 0.1259 | 0.1163 | 0.1135 | 0.127 | 0.1185 | Cloud | 0.2897 | 0.3269 | 0.3118 | 0.3006 | 0.3316 | 0.3177 | Auto price | 0.0809 | 0.1036 | 0.0910 | 0.0894 | 0.1045 | 0.0973 | Breast cancer | 0.2463 | 0.2641 | 0.2601 | 0.2519 | 0.2657 | 0.2597 | Balloon | 0.0570 | 0.0639 | 0.0611 | 0.0592 | 0.0672 | 0.0618 | Auto-MPG | 0.0724 | 0.0862 | 0.0799 | 0.0781 | 0.0891 | 0.0857 | Bank | 0.0415 | 0.0551 | 0.0496 | 0.0453 | 0.0537 | 0.0498 | Census (house8L) | 0.0746 | 0.0820 | 0.0802 | 0.0795 | 0.0867 | 0.0813 |
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