Research Article
Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine
Table 6
Comparison of algorithms for classification problems with RBF hidden nodes.
| Datasets | Algorithm | Number of nodes | Number of networks | Training time (s) | RMSE or Accuracy | Testing dev. |
| New-thyroid | OS-ELM | 20 | | 0.0118 | 93.45% | 89.92% | 0.0702 | EOS-ELM | 20 | 15 | 0.1682 | 93.87% | 89.86% | 0.0428 | SEOS-ELM (GASEN) | 20 | 15 | 1.9745 | 94.53% | 91.05% | 0.0332 | SEOS-ELM (PSOSEN) | 20 | 15 | 1.2315 | 94.68% | 91.32% | 0.0315 |
| Image segmentation | OS-ELM | 180 | | 2.6702 | 94.98% | 91.92% | 0.0324 | EOS-ELM | 180 | 5 | 13.2174 | 94.39% | 91.35% | 0.0148 | SEOS-ELM (GASEN) | 180 | 5 | 128.3215 | 95.62% | 95.06% | 0.0085 | SEOS-ELM (PSOSEN) | 180 | 5 | 90.2856 | 96.02% | 95.24% | 0.0079 |
| Satellite image | OS-ELM | 400 | | 45.2702 | 93.62% | 89.54% | 0.0056 | EOS-ELM | 400 | 10 | 448.1347 | 93.86% | 89.37% | 0.0034 | SEOS-ELM (GASEN) | 400 | 10 | 4263.1406 | 94.61% | 90.38% | 0.0022 | SEOS-ELM (PSOSEN) | 400 | 10 | 3145.8528 | 94.85% | 90.57% | 0.0019 |
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