Binary Particle Swarm Optimization for Feature Selection
Simple algorithm shows how binary particle swarm optimization (BPSO) used in feature selection problem.
https://github.com/JingweiToo/Binary-Particle-Swarm-Optimization-for-Feature-Selection
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Simple binary particle swarm optimization (BPSO) for feature selection tasks, which can select the potential features to improve the classification accuracy.
The < Main.m file > demos an example on how to use BPSO with classification error rate (computed by KNN) as the fitness function for feature selection problem using benchmark data-set.
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Cite As
Too, Jingwei, et al. “A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection.” Informatics, vol. 6, no. 2, MDPI AG, May 2019, p. 21, doi:10.3390/informatics6020021.
Too, Jingwei, et al. “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.” Computation, vol. 7, no. 1, MDPI AG, Feb. 2019, p. 12, doi:10.3390/computation7010012.
General Information
- Version 1.3 (61.2 KB)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.3 | See release notes for this release on GitHub: https://github.com/JingweiToo/Binary-Particle-Swarm-Optimization-for-Feature-Selection/releases/tag/1.3 |
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| 1.2 | Improve code for the fitness function |
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| 1.1.0 | change to hold-out |
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| 1.0.4 | - |
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| 1.0.3 | Changes Vmin=-Vmax |
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| 1.0.2 | - |
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| 1.0.1 | Add convergence plot |
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| 1.0.0 |
