Evolutionary Approaches to the Learning of Fuzzy Rule- Based Classification Systems
نویسندگان
چکیده
The learning of a Fuzzy Rule-Based Classification System (FRBCS) by means of a supervised inductive process fundamentally implies four tasks that are complementary among them: the selection of the most informative variables to the classification problem to solve, the generation of a set of rules, the selection of the subset of rules with the best co-operation and the least redundancy, and the establishment and tuning of the fuzzy partitions for the domains of the problem variables.
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تاریخ انتشار 1999