Research Article
Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine
Table 5
Comparison of algorithms for regression problems with RBF hidden nodes.
| Datasets | Algorithm | Number of nodes | Number of networks | Training time (s) | RMSE or Accuracy | Testing dev. | Training RMSE | Testing RMSE |
| Abalone | OS-ELM | 25 | | 0.3445 | 0.0753 | 0.0775 | 0.0027 | EOS-ELM | 25 | 25 | 8.5762 | 0.0752 | 0.0773 | 0.0023 | SEOS-ELM (GASEN) | 25 | 25 | 54.2453 | 0.0742 | 0.0760 | 0.0016 | SEOS-ELM (PSOSEN) | 25 | 25 | 49.3562 | 0.0741 | 0.0761 | 0.0017 |
| Mackey-Glass | OS-ELM | 120 | | 1.6854 | 0.0181 | 0.0185 | 0.0092 | EOS-ELM | 120 | 5 | 8.4304 | 0.0171 |
0.0171 | 0.0028 | SEOS-ELM (GASEN) | 120 | 5 | 79.3216 | 0.0155 | 0.0158 | 0.0021 | SEOS-ELM (PSOSEN) | 120 | 5 | 55.1469 | 0.0159 | 0.0156 | 0.0016 |
| California Housing | OS-ELM | 50 | | 1.8329 | 0.1298 | 0.1317 | 0.0017 | EOS-ELM | 50 | 5 | 9.0726 | 0.1296 | 0.1316 | 0.0011 | SEOS-ELM (GASEN) | 50 | 5 | 69.8636 | 0.1216 | 0.1262 | 0.0008 | SEOS-ELM (PSOSEN) | 50 | 5 | 64.9625 | 0.1202 | 0.1243 | 0.0009 |
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