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Reseach Article

Particle Swarm Optimization based Feature Selection

by Neha, Jyoti Vashishtha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 6
Year of Publication: 2016
Authors: Neha, Jyoti Vashishtha
10.5120/ijca2016910789

Neha, Jyoti Vashishtha . Particle Swarm Optimization based Feature Selection. International Journal of Computer Applications. 146, 6 ( Jul 2016), 11-17. DOI=10.5120/ijca2016910789

@article{ 10.5120/ijca2016910789,
author = { Neha, Jyoti Vashishtha },
title = { Particle Swarm Optimization based Feature Selection },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 6 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number6/25401-2016910789/ },
doi = { 10.5120/ijca2016910789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:37.769241+05:30
%A Neha
%A Jyoti Vashishtha
%T Particle Swarm Optimization based Feature Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 6
%P 11-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature Selection is a pre-processing step in knowledge discovery from data (KDD) which aims at retrieving relevant data from the database beforehand. It imparts quality to the results of data mining tasks by selecting optimal feature set from larger set of features. Various feature selection techniques have been proposed in past which, unfortunately, suffer from unavoidable problems such as high computational cost and getting stuck into the local optima. Evolutionary algorithms such as Particle Swarm Optimization (PSO) possess immense abilities to explore a large search space and rarely fall into local optima thus making them a nice choice for feature selection. In this paper, we have explored pros and cons of traditional and PSO based feature selection techniques and suggested some effective changes in existing approaches.

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Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization (PSO) Evolutionary Algorithm (EA) Feature Selection (FS).