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A New Classifier Design with Fuzzy Functions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4482)

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.

Keywords

Fuzzy classification fuzzy c-means clustering SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Dept. of Mechanical and Industrial Engineering, University of TorontoCanada
  2. 2.Dept. of Industrial Engineering TOBB-Economics and Technology UniversityTurkey
  3. 3.Dept. of Business Administration TOBB-Economics and Technology UniversityTurkey
  4. 4.Dept. of Business Administration Çankaya UniversityTurkey
  5. 5.Dept. of Business Administration Atılım UniversityTurkey

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