FILM: a fuzzy inductive learning method for automated knowledge acquisition

Inductive learning that creates decision trees from a set of existing cases is useful for automated knowledge acquisition. Most of the existing methods in literature are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a fuzzy inductive learning method that integrates the fuzzy set theory into the regular inductive learning processes. The method converts a decision tree induced from regular method into a fuzzy decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the popular discriminant analysis and ID3 in predictive accuracy. (C) 1997 Elsevier Science B.V.