A Framework for Designing a Fuzzy Rule-Based Classifier

Authors:
Jonas Guzaitis;Antanas Verikas;Adas Gelzinis;Marija Bacauskiene
Affiliations:
Department Electrical & Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania LT-51368;Department Electrical & Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania LT-51368 and Intelligent Systems Laboratory, Halmstad University, Halmstad, Sweden S-30118;Department Electrical & Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania LT-51368;Department Electrical & Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania LT-51368
Venue:
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Year:
2009

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Abstract

This paper is concerned with a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. The classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy.