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Reseach Article

Feature Selection using Modified Particle Swarm Optimization

by Khushboo Jain, Anuradha Purohit
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 7
Year of Publication: 2017
Authors: Khushboo Jain, Anuradha Purohit

Khushboo Jain, Anuradha Purohit . Feature Selection using Modified Particle Swarm Optimization. International Journal of Computer Applications. 161, 7 ( Mar 2017), 8-12. DOI=10.5120/ijca2017913229

@article{ 10.5120/ijca2017913229,
author = { Khushboo Jain, Anuradha Purohit },
title = { Feature Selection using Modified Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 7 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913229 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:06:29.118772+05:30
%A Khushboo Jain
%A Anuradha Purohit
%T Feature Selection using Modified Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 7
%P 8-12
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Feature selection is the process by which relevant features are selected from large datasets in order to improve the performance of the classification systems. There are various approaches that are used for feature selection such as Soft Computing, Hill Climbing etc. Particle Swarm Optimization is now a days popularly used soft computing technique for feature selection due to its searching ability, simplicity and low computation cost. But the main problem with Particle Swarm Optimization is premature convergence which in turn affects the classification performance. In this paper, a modified Particle Swarm Optimization is proposed for feature selection. To handle the problem of premature convergence, a flipping operator is introduced before the updation of velocity and position of the particle. Fitness of each particle is computed using Support Vector Machine based fitness function. To establish the effectiveness of proposed approach, testing is done on various benchmark datasets like wine, zoo, sonar etc. Results obtained on these datasets are compared with the standard approach and satisfactory improvements are observed.

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Index Terms

Computer Science
Information Sciences


Feature Selection Particle Swarm Optimization Classification Support Vector Machine.