Genetic Algorithm for Feature Selection

Version 1.3 (62,1 ko) par Jingwei Too

Simple algorithm shows how the genetic algorithm (GA) used in the feature selection problem.

https://github.com/JingweiToo/Genetic-Algorithm-for-Feature-Selection

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Mise à jour 19 déc. 2020

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Simple genetic algorithm (GA) for feature selection tasks, which can select the potential features to improve the classification accuracy.

The < Main.m file > illustrates the example of how GA can solve the feature selection problem using a benchmark data-set.

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Citation pour cette source

Too, Jingwei, and Abdul Rahim Abdullah. “A New and Fast Rival Genetic Algorithm for Feature Selection.” The Journal of Supercomputing, Springer Science and Business Media LLC, July 2020, doi:10.1007/s11227-020-03378-9.

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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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Compatibilité avec les versions de MATLAB

Créé avec R2018a

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Version	Publié le	Notes de version
1.3	19 déc. 2020	See release notes for this release on GitHub: https://github.com/JingweiToo/Genetic-Algorithm-for-Feature-Selection/releases/tag/1.3	Télécharger
1.2.1	22 oct. 2020	Solve one bug in the fitness function	Télécharger
1.2	22 oct. 2020	Improve code for the fitness function	Télécharger
1.1.0	23 juil. 2020	change to hold-out	Télécharger
1.0.0	22 juil. 2020		Télécharger

Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.

MLA	Too, Jingwei, and Abdul Rahim Abdullah. “A New and Fast Rival Genetic Algorithm for Feature Selection.” The Journal of Supercomputing, Springer Science and Business Media LLC, July 2020, doi:10.1007/s11227-020-03378-9.
APA	Too, J., & Abdullah, A. R. (2020). A new and fast rival genetic algorithm for feature selection. The Journal of Supercomputing. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs11227-020-03378-9
BibTeX	@article{Too_2020, doi = {10.1007/s11227-020-03378-9}, url = {https://doi.org/10.1007%2Fs11227-020-03378-9}, year = 2020, month = {jul}, publisher = {Springer Science and Business Media {LLC}}, author = {Jingwei Too and Abdul Rahim Abdullah}, title = {A new and fast rival genetic algorithm for feature selection}, journal = {The Journal of Supercomputing} }

MLA	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.
APA	Too, J., Abdullah, A., Saad, N. M., & Tee, W. (2019). EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization. Computation, 7(1), 12. MDPI AG. Retrieved from https://doi.org/10.3390%2Fcomputation7010012
BibTeX	@article{Too_2019, doi = {10.3390/computation7010012}, url = {https://doi.org/10.3390%2Fcomputation7010012}, year = 2019, month = {feb}, publisher = {{MDPI} {AG}}, volume = {7}, number = {1}, pages = {12}, author = {Jingwei Too and Abdul Abdullah and Norhashimah Mohd Saad and Weihown Tee}, title = {{EMG} Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization}, journal = {Computation} }