Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6885
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dc.contributor.authorÇelikyılmaz, Aslı-
dc.contributor.authorTürkşen, İsmail Burhan-
dc.contributor.authorAktaş, Ramazan-
dc.contributor.authorDoğanay, M. Mete-
dc.contributor.authorCeylan, N. Başak-
dc.date.accessioned2021-09-11T15:44:04Z-
dc.date.available2021-09-11T15:44:04Z-
dc.date.issued2009en_US
dc.identifier.issn0957-4174-
dc.identifier.issn1873-6793-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2007.11.039-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6885-
dc.description.abstractIn 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. (c) 2007 Elsevier Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy classificationen_US
dc.subjectImproved fuzzy clusteringen_US
dc.subjectFuzzy Functionsen_US
dc.subjectData miningen_US
dc.subjectEarly warning systemen_US
dc.subjectDecision support systemsen_US
dc.titleIncreasing Accuracy of Two-Class Pattern Recognition With Enhanced Fuzzy Functionsen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Economics and Administrative Sciences, Department of Managementen_US
dc.departmentFakülteler, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümütr_TR
dc.identifier.volume36en_US
dc.identifier.issue2en_US
dc.identifier.startpage1337en_US
dc.identifier.endpage1354en_US
dc.identifier.wosWOS:000262178000039en_US
dc.identifier.scopus2-s2.0-56649124794en_US
dc.institutionauthorAktaş, Ramazan-
dc.identifier.doi10.1016/j.eswa.2007.11.039-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept04.03. 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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