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

•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.•The second phase develops the geometry of hyperboxes using PSO algorithm.•The PSO is used to optimize the classification rate and ex...

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Veröffentlicht in:Knowledge-based systems Jg. 76; S. 200 - 218
Hauptverfasser: Salehi, Saber, Selamat, Ali, Reza Mashinchi, M., Fujita, Hamido
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2015
Schlagworte:
Algorithms
Bundling
Classifiers
Construction
DBSCAN clustering algorithm
Fuzzy
Fuzzy set theory
Granular classifier
Hyperbox geometry of classifiers
Knowledge base
Mathematical analysis
Membership functions
Particle swarm optimization
Swarm intelligence
Hyperbox geometry of classifiers
Membership functions
Granular classifier
Particle swarm optimization
DBSCAN clustering algorithm
ISSN:0950-7051, 1872-7409
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Zusammenfassung:•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.•The second phase develops the geometry of hyperboxes using PSO algorithm.•The PSO is used to optimize the classification rate and expanding the hyperboxes.•The third phase identifies the noise points to improve the geometry of 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 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.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2014.12.017