Information measures in fuzzy decision trees

Decision trees are a popular form of classification models. It is well known that classical trees lack the ability of modelling vagueness. By connecting fuzzy systems and classical decision trees, we try to achieve classifiers that can model vagueness and are comprehensible. We discuss the core problem of how to compute the information measure used in the induction of fuzzy trees and propose some improvements. In addition, we consider fuzzy rule bases derived from fuzzy decision trees and present some heuristic strategies to prune them. We report the results of experiments in which we compare our approach to other well-known classification methods.