Research Article
Multistrategy Self-Organizing Map Learning for Classification Problems
Table 3
Parameter settings for ESOMPSO.
| Parameter | Dataset | Iris | XOR | Cancer | Glass | Pendigits |
| Input vector (Training) | 120 | 6 | 379 | 149 | 7494 | Input vector (Testing) | 30 | 2 | 190 | 65 | 3498 | Input dimension | 4 | 4 | 30 | 9 | 16 | SOM's Mapping Dimension | 10 × 10 | 10 × 10 | 10 × 10 | 10 × 10 | 10 × 10 | SOM lattice structure | Standard hexagonal | Standard hexagonal | Standard hexagonal | Standard hexagonal | Standard hexagonal | ESOM lattice structure | Improved Hexagonal | Improved hexagonal | Improved hexagonal | Improved hexagonal | Improved Hexagonal | Learning rate | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | Number of runs | 10 times | 10 times | 10 times | 10 times | 10 times | Epoch | 1000 | 1000 | 1000 | 1000 | 1000 | | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | Number of particles | 100 | 100 | 100 | 100 | 100 | PSO problem dimension | 10 × 10 | 10 × 10 | 10 × 10 | 10 × 10 | 10 × 10 | Stop condition (minimum error) | 0.0000193 | 0.0000193 | 0.0000193 | 0.0000193 | 0.0000193 |
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