A genetic algorithm method for optimizing fuzzy decision trees

Fuzzy decision trees exploit the popularity of decision tree algorithms for practical knowledge acquisition and the representative power of the fuzzy technology. They are extensions of IDS trees, with the tree-building routine modified to utilize fuzzy instead of strict domains, and with new inferences combining fuzzy defuzzification with the inductive methodology. As ID3 trees, they require that real-valued and multivalued domains be partitioned prior to tree construction. In this paper, we introduce a methodology aimed at relaxing this requirement. This is done by optimizing the domain partitions. This optimization is based on genetic algorithms, designed to process constraints associated with this task. A simple illustration is also given.