Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6109
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Çelikyılmaz, Aslı | - |
dc.contributor.author | Türkşen, İsmail Burhan | - |
dc.contributor.author | Aktaş, Ramazan | - |
dc.contributor.author | Doğanay, M. Mete | - |
dc.contributor.author | Ceylan, N. Başak | - |
dc.date.accessioned | 2021-09-11T15:34:58Z | - |
dc.date.available | 2021-09-11T15:34:58Z | - |
dc.date.issued | 2007 | en_US |
dc.identifier.citation | 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007) -- MAY 14-16, 2007 -- Toronto, CANADA | en_US |
dc.identifier.isbn | 978-3-540-72529-9 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6109 | - |
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.sponsorship | Infobright Inc, MaRS Discovery Dist, York Univ, Int Rough Set Soc, Int Fuzzy Syst Assoc, Chinese Assoc Artificial Intelligence, Rough Sets & Soft Computat Soc, Natl Res Council Canada | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer-Verlag Berlin | en_US |
dc.relation.ispartof | Rough Sets, Fuzzy Sets, Data Mining And Granular Computing, Proceedings | 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.relation.ispartofseries | Lecture Notes in Artificial Intelligence | en_US |
dc.department | Faculties, Faculty of Economics and Administrative Sciences, Department of Management | en_US |
dc.department | Fakülteler, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü | tr_TR |
dc.identifier.volume | 4482 | en_US |
dc.identifier.startpage | 136 | en_US |
dc.identifier.endpage | + | en_US |
dc.identifier.wos | WOS:000246403500016 | en_US |
dc.identifier.scopus | 2-s2.0-38049038377 | en_US |
dc.institutionauthor | Aktaş, Ramazan | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007) | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 04.03. Department of Management | - |
Appears in Collections: | İşletme Bölümü / Department of Management Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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