Learning Fuzzy Concept Definitions

A b s t r a c t --The symbolic approach to machine learning has developed algorithms for learning F i r s t O r d e r L o g i c c o n c e p t d e f i n i t i o n s . Nevertheless, most of them are limited because of their impossibility to cope with numeric features, typical of real-world data. In this paper, a method to face this problem is proposed. In particular, an extended version of the system ML-SMART is described, which is capable to automatically adjust 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 one, 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, trying to get closer to an optimum assignment. The system is evaluated on a complex a r t i f i c i a l d o m a i n , t h a t s h o w s t h e g o o d potentialities of this approach.