A New Classifier Design With Fuzzy Functions
dc.contributor.author | Celikytlmaz, Ash | |
dc.contributor.author | Tuerksen, I. Burhan | |
dc.contributor.author | Aktas, Ramazan | |
dc.contributor.author | Doganay, M. Mete | |
dc.contributor.author | Ceylan, N. Basak | |
dc.contributor.other | Tourism Management | |
dc.date.accessioned | 2024-10-06T10:57:14Z | |
dc.date.available | 2024-10-06T10:57:14Z | |
dc.date.issued | 2007 | |
dc.department | Atılım University | en_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, Turkey | en_US |
dc.description.abstract | This 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.woscitationindex | Conference Proceedings Citation Index - Science | |
dc.identifier.citationcount | 9 | |
dc.identifier.endpage | + | en_US |
dc.identifier.isbn | 9783540725299 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.scopusquality | Q3 | |
dc.identifier.startpage | 136 | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.14411/8693 | |
dc.identifier.volume | 4482 | en_US |
dc.identifier.wos | WOS:000246403500016 | |
dc.institutionauthor | Ceylan, Nildağ Başak | |
dc.language.iso | en | en_US |
dc.publisher | Springer-verlag Berlin | en_US |
dc.relation.ispartof | 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007) -- MAY 14-16, 2007 -- Toronto, CANADA | en_US |
dc.relation.ispartofseries | Lecture Notes in Artificial Intelligence | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | fuzzy classification | en_US |
dc.subject | fuzzy c-means clustering | en_US |
dc.subject | SVM | en_US |
dc.title | A New Classifier Design With Fuzzy Functions | en_US |
dc.type | Conference Object | en_US |
dc.wos.citedbyCount | 8 | |
dspace.entity.type | Publication | |
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