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Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions

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dc.contributor.author Çelikyılmaz, Aslı
dc.contributor.author Türkşen, İsmail Burhan
dc.contributor.author Aktaş, Ramazan
dc.contributor.author Doğanay, Mehmet Mete
dc.contributor.author Ceylan, Nildağ Başak
dc.date.accessioned 2016-04-12T11:58:01Z
dc.date.available 2016-04-12T11:58:01Z
dc.date.issued 2009-03
dc.identifier.citation Çelikyılmaz, A., Türkşen, İ.B., Aktaş, R., Doğanay, M.M., Ceylan, N.B. (2009). Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions. Expert Systems with Applications, 36(2), 1337-1354. http://dx.doi.org/10.1016/j.eswa.2007.11.039 tr_TR
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/20.500.12416/900
dc.description.abstract In building an approximate fuzzy classifier system, significant effort is laid oil estimation and fine tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy Clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based oil it dual optimization method, which yields simultaneous estimates of the parameters of (c-classification functions together with fuzzy c partitioning of dataset based oil a distance measure. The merit of novel IFCF is that the information oil natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results Of the new modeling approach indicate that the new IFCF is it promising method for two-class pattern recognition problems tr_TR
dc.language.iso eng tr_TR
dc.publisher Pergamon-Elsevier Science Ltd tr_TR
dc.relation.isversionof 10.1016/j.eswa.2007.11.039 tr_TR
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Fuzzy Classification tr_TR
dc.subject Improved Fuzzy Clustering tr_TR
dc.subject Fuzzy Functions tr_TR
dc.subject Data Mining tr_TR
dc.subject Early Warning System tr_TR
dc.subject Decision Support Systems tr_TR
dc.title Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions tr_TR
dc.type article tr_TR
dc.relation.journal Expert Systems with Applications tr_TR
dc.contributor.authorID 122648 tr_TR
dc.contributor.authorID 1109 tr_TR
dc.contributor.authorID 112010 tr_TR
dc.contributor.authorID 108611 tr_TR
dc.identifier.volume 36 tr_TR
dc.identifier.issue 2 tr_TR
dc.identifier.startpage 1337 tr_TR
dc.identifier.endpage 1354 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi İdari Bilimler Fakültesi, İşletme Bölümü tr_TR


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