Constructing a Fuzzy Decision Tree by Integrating Fuzzy Sets and Entropy

Decision tree induction is one of common approaches for extracting knowledge from a sets of feature-based examples. In real world, many data occurred in a fuzzy and uncertain form. The decision tree must able to deal with such fuzzy data. This paper presents a tree construction procedure to build a fuzzy decision tree from a collection of fuzzy data by integrating fuzzy set theory and entropy. It proposes a fuzzy decision tree induction method for fuzzy data of which numeric attributes can be represented by fuzzy number, interval value as well as crisp value, of which nominal attributes are represented by crisp nominal value, and of which class has confidence factor. It also presents an experiment result to show the applicability of the proposed method. Key-Words: Fuzzy Decision Tree, Fuzzy Sets, Entropy, Information Gain, Classification, Data Mining