Binary Grey Wolf Optimization for Feature Selection

Versione 1.3 (62,1 KB) da Jingwei Too
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.
4.6
(5)
1,8K download
Aggiornato 19 dic 2020
Visualizza la licenza su GitHub

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

Compatibilità della release di MATLAB
Creato con R2018a
Compatibile con qualsiasi release
Compatibilità della piattaforma
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Categorie
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Versione Pubblicato Note della release
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
1.2

Improve code for the fitness function
1.1.0

Change to hold-out
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.
1.0.2

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1.0.1

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1.0.0

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.