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Classification Models Based On Tanaka's Fuzzy Linear Regression Approach: the Case of Customer Satisfaction Modeling

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dc.contributor.author Bayrak, Özlem Türker
dc.date.accessioned 2020-04-18T17:15:26Z
dc.date.available 2020-04-18T17:15:26Z
dc.date.issued 2010
dc.identifier.citation Sekkeli, Gizem; Koksal, Gulser; Batman, Inci; et al. "Classification models based on Tanaka's fuzzy linear regression approach: The case of customer satisfaction modeling", Journal of Intelligent & Fuzzy Systems, Vol. 21, No. 5, (2010). tr_TR
dc.identifier.issn 1064-1246
dc.identifier.issn 1875-8967
dc.identifier.uri http://hdl.handle.net/20.500.12416/3358
dc.description.abstract Fuzzy linear regression (FLR) approaches are widely used for modeling relations between variables that involve human judgments, qualitative and imprecise data. Tanaka's FLR analysis is the first one developed and widely used for this purpose. However, this method is not appropriate for classification problems, because it can only handle continuous type dependent variables rather than categorical. In this study, we propose three alternative approaches for building classification models, for a customer satisfaction survey data, based on Tanaka's FLR approach. In these models, we aim to reflect both random and fuzzy types of uncertainties in the data in different ways, and compare their performances using several classification performance measures. Thus, this study contributes to the field of fuzzy classification by developing Tanaka based classification models. tr_TR
dc.language.iso eng tr_TR
dc.publisher IOS Press tr_TR
dc.relation.isversionof 10.3233/IFS-2010-0466 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Fuzziness tr_TR
dc.subject Fuzzy Classification tr_TR
dc.subject Fuzzy Linear Regression (FLR) tr_TR
dc.subject Customer Satisfaction tr_TR
dc.title Classification Models Based On Tanaka's Fuzzy Linear Regression Approach: the Case of Customer Satisfaction Modeling tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Intelligent & Fuzzy Systems tr_TR
dc.contributor.authorID 56416 tr_TR
dc.identifier.volume 21 tr_TR
dc.identifier.issue 5 tr_TR
dc.identifier.startpage 341 tr_TR
dc.identifier.endpage 351 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi İdari Bilimler Fakültesi, İstatistik Bilim Dalı tr_TR


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