The aim of this paper is to demonstrate the feasibility of fuzzy measures of subsethood in learning from examples. Using the relationship between (fuzzy) set containment and (fuzzy) logical implication, a method of generating if-then rules that describe a fuzzy dataset is given. In order to obtain an efficient subset of the generated rules, we apply a simple genetic algorithm. The proposed method is illustrated with a fuzzified well-known learning set. The results on this set clearly improve other approaches.
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