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Genetic Algorithms, being global search method, have been extensively applied for discovery of automated classification rules. Fuzzy Logic was integrated with genetic algorithms for discovery of Fuzzy Classification Rules (FCRs) which are more interpretable and cope better with pervasive uncertainty and vagueness in real world decision making situations. At one hand, most of the Genetic Algorithm approaches have been implemented for datasets with categorical attributes only; at the other Genetic-Fuzzy approaches have the limitation to deal only with continuous attributes. This paper proposes genetic-fuzzy approach for discovery of fuzzy decision rules from datasets containing both categorical as well as continuous attributes.
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