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RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 6
Year of Publication: 2017
Authors:
Anupriya Gupta, Anuradha Purohit
10.5120/ijca2017913228

Anupriya Gupta and Anuradha Purohit. RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach. International Journal of Computer Applications 161(6):1-5, March 2017. BibTeX

@article{10.5120/ijca2017913228,
	author = {Anupriya Gupta and Anuradha Purohit},
	title = {RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {6},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume161/number6/27149-2017913228},
	doi = {10.5120/ijca2017913228},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Feature selection plays an important role in improving the classification accuracy by handling redundant or irrelevant features present in the dataset. Various soft computing based hybrid approaches like neuro-fuzzy, genetic-fuzzy, rough set-neuro etc. are proposed by researchers to perform feature selection. The existing approaches gives higher complexity and computational cost with low classification accuracy. Hence to improve the complexity and classification accuracy, a hybrid approach based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Rough Set Theory (RST) to perform feature selection is proposed. In the proposed approach, GA is used as a searching algorithm. To explore search space more efficiently, GA is combined with a PSO based local search operation. Rough Set Attribute Reduction (RSAR) method based on RST is used to compute core reducts. The proposed algorithm is tested on various benchmark datasets. Satisfactory improvements in terms of complexity and classification accuracy have been achieved.

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Keywords

Feature Selection, Particle Swarm Optimization, Genetic Algorithm, Rough Set Theory.