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Binary Grey Wolf Optimization for Feature Selection

version 1.1.0 (59.5 KB) by Jingwei Too
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.

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Updated 23 Jul 2020

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This toolbox offers two types of binary grey wolf optimization (BGWO) methods

The "Main" script demos the examples of how BGWO solves the feature selection problem using benchmark data-set.

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Detail of BGWO can be found in the following papers:
[1] Too, J., Abdullah, A.R., Mohd Saad, N., Mohd Ali, N. and Tee, W., 2018. A new competitive binary Grey Wolf Optimizer to solve the feature selection problem in EMG signals classification. Computers, 7(4), p.58.
DOI: https://doi.org/10.3390/computers7040058

[2] Too, J. and Abdullah, A.R., 2020. Opposition based competitive grey wolf optimizer for EMG feature selection. Evolutionary Intelligence.
DOI: https://doi.org/10.1007/s12065-020-00441-5

Cite As

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:10.3390/computers7040058.

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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:10.1007/s12065-020-00441-5.

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Comments and Ratings (2)

Matlab

Tee Wei Hown

Updates

1.1.0

Change to hold-out

1.0.6

-

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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MATLAB Release Compatibility
Created with R2018a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Binary Grey Wolf Optimization for Feature Selection