Learning compact fuzzy rule-based classification systems with genetic programming
نویسندگان
چکیده
The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this paper we propose a geneticprogramming-based method, where the disjunctive normal form (DNF) fuzzy rules compete in order to obtain an FRBCS with high interpretability and accuracy. The good results obtained with several classification problems support our proposal.
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Article history: Received 7 October 2008 Received in revised form 25 November 2009 Accepted 22 December 2009
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