Binary Particle Swarm Optimization for Feature Selection

Version 1.3 (61.2 KB) by Jingwei Too
Simple algorithm shows how binary particle swarm optimization (BPSO) used in feature selection problem.
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Updated 19 Dec 2020
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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.

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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.

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

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Version Published Release Notes
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
1.2

Improve code for the fitness function

1.1.0

change to hold-out
1.0.4

-
1.0.3

Changes Vmin=-Vmax
1.0.2

-
1.0.1

Add convergence plot

1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.