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The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier

Granulation extracts a bundle of similar patterns by decomposing universe. Hyperboxes are granular classifiers to confront the uncertainties in granular computing. This paper proposes a granular classifier to discover hyperboxes in three phases. The first phase of the proposed model uses the set cal...

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主要な著者: Salehi, Saber, Selamat, Ali, Mashinchi, M. Reza, Fujita, Hamido
フォーマット: 論文
出版事項: Elsevier 2015
主題:
T58.5-58.64 Information technology
オンライン･アクセス:http://eprints.utm.my/id/eprint/58999/
http://dx.doi.org/10.1016/j.knosys.2014.12.017
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spelling my.utm.589992017-02-01T01:05:47Z http://eprints.utm.my/id/eprint/58999/ The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier Salehi, Saber Selamat, Ali Mashinchi, M. Reza Fujita, Hamido T58.5-58.64 Information technology Granulation extracts a bundle of similar patterns by decomposing universe. Hyperboxes are granular classifiers to confront the uncertainties in granular computing. This paper proposes a granular classifier to discover hyperboxes in three phases. The first phase of the proposed model uses the set calculus to build the hyperboxes; where, the means of the DBSCAN clustering algorithm constructs the structure. The second phase develops the geometry of hyperboxes to improve the classification rate. It uses the Particle Swarm Optimization (PSO) algorithm to optimize the seed-points and expand the hyperboxes. Finally, the third phase identifies the noise points; where, the patterns in the second phase did not belong to any hyperboxes. We have used the capability of membership function of a fuzzy set to improve the geometry of classifier. The performance of a proposed model is carried out in terms of coverage, misclassification error and accuracy. Experimental results reveal that the proposed model can adaptively choose an appropriate granularity. Elsevier 2015-03 Article PeerReviewed Salehi, Saber and Selamat, Ali and Mashinchi, M. Reza and Fujita, Hamido (2015) The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier. Knowledge-Based Systems, 76 . pp. 200-218. ISSN 9507-051 http://dx.doi.org/10.1016/j.knosys.2014.12.017 DOI:10.1016/j.knosys.2014.12.017
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T58.5-58.64 Information technology
spellingShingle T58.5-58.64 Information technology
Salehi, Saber
Selamat, Ali
Mashinchi, M. Reza
Fujita, Hamido
The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
description Granulation extracts a bundle of similar patterns by decomposing universe. Hyperboxes are granular classifiers to confront the uncertainties in granular computing. This paper proposes a granular classifier to discover hyperboxes in three phases. The first phase of the proposed model uses the set calculus to build the hyperboxes; where, the means of the DBSCAN clustering algorithm constructs the structure. The second phase develops the geometry of hyperboxes to improve the classification rate. It uses the Particle Swarm Optimization (PSO) algorithm to optimize the seed-points and expand the hyperboxes. Finally, the third phase identifies the noise points; where, the patterns in the second phase did not belong to any hyperboxes. We have used the capability of membership function of a fuzzy set to improve the geometry of classifier. The performance of a proposed model is carried out in terms of coverage, misclassification error and accuracy. Experimental results reveal that the proposed model can adaptively choose an appropriate granularity.
format Article
author Salehi, Saber
Selamat, Ali
Mashinchi, M. Reza
Fujita, Hamido
author_facet Salehi, Saber
Selamat, Ali
Mashinchi, M. Reza
Fujita, Hamido
author_sort Salehi, Saber
title The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
title_short The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
title_full The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
title_fullStr The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
title_full_unstemmed The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
title_sort synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
publisher Elsevier
publishDate 2015
url http://eprints.utm.my/id/eprint/58999/
http://dx.doi.org/10.1016/j.knosys.2014.12.017
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score 13.250246

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