Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6962
Title: Kernel Based Hybrid Fuzzy Clustering for Non-Linear Fuzzy Classifiers
Authors: Çelikyılmaz, Aslı
Türkşen, İsmail Burhan
Keywords: hybrid fuzzy clustering
kernels
pattern clustering methods
Issue Date: 2009
Publisher: IEEE
Source: Annual Meeting of the North-American-Fuzzy-Information-Processing-Society -- JUN 14-17, 2009 -- Cincinnati, OH
Abstract: In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order to learn a fuzzy classifier system. The two objectives are balanced with a regularization term. An additional merit of the novel method is that the information on natural groupings of the data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function learnt from the non-linear SVC to improve the accuracy of the classifier model. The comparative experiments demonstrate the effectiveness of the proposed method in building a classifier model for a detection system.
URI: https://hdl.handle.net/20.500.11851/6962
ISBN: 978-1-4244-4575-2
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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