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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2017

12.07.2016 | Original Article

Fuzzy decision function estimation using fuzzified particle swarm optimization

verfasst von: Hadi Shahraki, Seyed-Hamid Zahiri

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2017

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Abstract

Present paper reports an upgrade of particle swarm optimization (PSO) algorithm for fuzzy environment by the definition of the particles as fuzzy numbers and reformulating their motion by fuzzy equations. The proposed fuzzified PSO is used to construct a set of fuzzy hyperplanes in the feature space to distinguish different classes. Fuzzy decision hyperplane assign a fuzzy membership to each sample rather than allocating to a specific class. Also the weight vector of fuzzy decision hyperplane is a set of fuzzy numbers. The proposed fuzzy classifier is called fuzzified particle swarm classifier (FPS-classifier) and its performance is evaluated by some artificial and well known benchmarks data sets.
Vorheriger Artikel Improving landmark detection accuracy for self-localization through baseboard recognition
Nächster Artikel Background subtraction based on modified online robust principal component analysis

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Metadaten
Titel
Fuzzy decision function estimation using fuzzified particle swarm optimization
verfasst von
Hadi Shahraki
Seyed-Hamid Zahiri
Publikationsdatum
12.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0561-8

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