Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6109
Title: A new classifier design with fuzzy functions
Authors: Çelikyılmaz, Aslı
Türkşen, İsmail Burhan
Aktaş, Ramazan
Doğanay, M. Mete
Ceylan, N. Başak
Keywords: fuzzy classification
fuzzy c-means clustering
SVM
Issue Date: 2007
Publisher: Springer-Verlag Berlin
Source: 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007) -- MAY 14-16, 2007 -- Toronto, CANADA
Series/Report no.: Lecture Notes in Artificial Intelligence
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.
URI: https://hdl.handle.net/20.500.11851/6109
ISBN: 978-3-540-72529-9
ISSN: 0302-9743
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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