Construction of Efficient Rulesets from Fuzzy Data through Simulated Annealing
This paper proposes a simulated annealing-based approach for obtaining compact efficient classification systems from fuzzy data. Different methods for generating decision rules from fuzzy data share a problem in multidimensional spaces: their high cardinality. In order to solve it, the method of simulated annealing is proposed. This approach is illustrated with two well-known learning sets.
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