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A genetic algorithm based fuzzy inference system for pattern classification and rule extraction

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dc.citedby1
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorLi X.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid23100514300en_US
dc.date.accessioned2023-05-29T06:54:52Z
dc.date.available2023-05-29T06:54:52Z
dc.date.issued2018
dc.description.abstractSetting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity. � 2018 Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14419/ijet.v7i4.35.22762
dc.identifier.epage368
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85059233832
dc.identifier.spage361
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059233832&doi=10.14419%2fijet.v7i4.35.22762&partnerID=40&md5=12d5de4d1095451dc7a76763b19966a1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24056
dc.identifier.volume7
dc.publisherScience Publishing Corporation Incen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Engineering and Technology(UAE)
dc.titleA genetic algorithm based fuzzy inference system for pattern classification and rule extractionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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