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.
We acknowledge the support from the agency for international science and technology development programmes in Lithuania (COST IC0602).
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Guzaitis, J., Verikas, A., Gelzinis, A., Bacauskiene, M. (2009). A Framework for Designing a Fuzzy Rule-Based Classifier. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_38
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DOI: https://doi.org/10.1007/978-3-642-04428-1_38
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