Binary Grey Wolf Optimization for Feature Selection
This toolbox offers two types of binary grey wolf optimization (BGWO) methods
The < Main.m file > demos the examples of how BGWO solves the feature selection problem using benchmark data-set.
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Please consider citing my article
[1] Too, Jingwei, et al. “A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification.” Computers, vol. 7, no. 4, MDPI AG, Nov. 2018, p. 58, DOI:https://doi.org/10.3390/computers7040058
[2] Too, Jingwei, and Abdul Rahim Abdullah. “Opposition Based Competitive Grey Wolf Optimizer for EMG Feature Selection.” Evolutionary Intelligence, Springer Science and Business Media LLC, July 2020, DOI: https://doi.org/10.1007/s12065-020-00441-5
Compatibilité avec les versions de MATLAB
Plateformes compatibles
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
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Version | Publié le | Notes de version | |
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1.3 | See release notes for this release on GitHub: https://github.com/JingweiToo/Binary-Grey-Wolf-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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1.0.6 | - |
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1.0.5 | - |
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1.0.4 | - |
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1.0.3 | Simplify BGWO1 program. |
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1.0.2 | - |
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1.0.1 | - |
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1.0.0 |