A New Classifier Design With Fuzzy Functions

dc.contributor.authorCelikytlmaz, Ash
dc.contributor.authorTuerksen, I. Burhan
dc.contributor.authorAktas, Ramazan
dc.contributor.authorDoganay, M. Mete
dc.contributor.authorCeylan, N. Basak
dc.contributor.otherTourism Management
dc.date.accessioned2024-10-06T10:57:14Z
dc.date.available2024-10-06T10:57:14Z
dc.date.issued2007
dc.departmentAtılım Universityen_US
dc.department-temp[Celikytlmaz, Ash; Tuerksen, I. Burhan] Univ Toronto, Dept Mech & Ind Engn, 100 Coll St, Toronto, ON, Canada; [Tuerksen, I. Burhan] TOBB Econ & Technol Univ, Dept Ind Engn, Ankara, Turkey; [Aktas, Ramazan] TOBB Econ & Technol Univ, Dept Business Adm, Ankara, Turkey; [Doganay, M. Mete] Cankaya Univ, Dept Business Adm, Ankara, Turkey; [Ceylan, N. Basak] Atilim Univ, Dept Business Adm, Ankara, Turkeyen_US
dc.description.abstractThis paper presents a new fuzzy classifier design, which constructs one classifier for each fuzzy partition of a given system. The new approach, namely Fuzzy Classifier Functions (FCF), is an adaptation of our generic design on Fuzzy Functions to classification problems. This approach couples any fuzzy clustering algorithm with any classification method, in a unique way. The presented model derives fuzzy functions (rules) from data to classify patterns into number of classes. Fuzzy c-means clustering is used to capture hidden fuzzy patterns and a linear or a non-linear classifier function is used to build one classifier model for each pattern identified. The performance of each classifier is enhanced by using corresponding membership values of the data vectors as additional input variables. FCF is proposed as an alternate representation and reasoning schema to fuzzy rule base classifiers. The proposed method is evaluated by the comparison of experiments with the standard classifier methods using cross validation on test patterns.en_US
dc.description.woscitationindexConference Proceedings Citation Index - Science
dc.identifier.citationcount9
dc.identifier.endpage+en_US
dc.identifier.isbn9783540725299
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopusqualityQ3
dc.identifier.startpage136en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14411/8693
dc.identifier.volume4482en_US
dc.identifier.wosWOS:000246403500016
dc.institutionauthorCeylan, Nildağ Başak
dc.language.isoenen_US
dc.publisherSpringer-verlag Berlinen_US
dc.relation.ispartof11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007) -- MAY 14-16, 2007 -- Toronto, CANADAen_US
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfuzzy classificationen_US
dc.subjectfuzzy c-means clusteringen_US
dc.subjectSVMen_US
dc.titleA New Classifier Design With Fuzzy Functionsen_US
dc.typeConference Objecten_US
dc.wos.citedbyCount8
dspace.entity.typePublication
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