A New Classifier Design with Fuzzy Functions

Authors:
Aslı Çelikyılmaz;I. Burhan Türkşen;Ramazan Aktaş;M. Mete Doğanay;N. Başak Ceylan
Affiliations:
Dept. of Mechanical and Industrial Engineering, University of Toronto, Canada;Dept. of Mechanical and Industrial Engineering, University of Toronto, Canada and Dept. of Industrial Engineering TOBB-Economics and Technology University, Turkey;Dept. of Business Administration TOBB-Economics and Technology University, Turkey;Dept. of Business Administration Çankaya University, Turkey;Dept. of Business Administration Atılım University, Turkey
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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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.