Learning fuzzy concept definitions

The symbolic approach to machine learning has developed algorithms for learning first-order logic concept definitions. Nevertheless, most of them are limited because of their inability to cope with numeric features, typical of real-world data. A method to overcome this problem is proposed. In particular, an extended version of the system ML-SMART is described, which is capable of automatically adjusting the values of fuzzy sets used to define the semantics of the predicates in the concept description language. The learning strategy works in two separate phases. In the first phase, the structure of the concept definition is learned by choosing tentative values for the fuzzy sets. In the second phase, the values are refined using a simple genetic algorithm by trying to get closer to an optimum assignment. The system is evaluated on a complex artificial domain that shows the good potentialities of this approach.<
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