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A general framework for designing a fuzzy rule-based classifier

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Abstract

This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, 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. By contrast, the classification accuracy of unseen data is increased due to the elimination.

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Authors and Affiliations

  1. Intelligent Systems Laboratory, Halmstad University, Box 823, 30118, Halmstad, Sweden

    Antanas Verikas

  2. Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Antanas Verikas, Jonas Guzaitis, Adas Gelzinis & Marija Bacauskiene

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  1. Antanas Verikas
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  2. Jonas Guzaitis
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  3. Adas Gelzinis
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  4. Marija Bacauskiene
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Correspondence to Antanas Verikas.

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Verikas, A., Guzaitis, J., Gelzinis, A. et al. A general framework for designing a fuzzy rule-based classifier. Knowl Inf Syst 29, 203–221 (2011). https://doi.org/10.1007/s10115-010-0340-x

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  • DOI: https://doi.org/10.1007/s10115-010-0340-x

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