A fuzzy decision tree induction method for fuzzy data

Decision tree induction is one of useful approaches for extracting classification knowledge from a set of feature-based examples. Due to observation error, uncertainty, subjective judgement, and so on, many data occurring in real world are obtained in fuzzy description. Although several fuzzy decision tree induction methods have been developed for fuzzy data they are improper to deal with some types of fuzzy data. This paper proposes a fuzzy decision tree induction method for fuzzy data of which numeric attributes are 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 presents a tree construction procedure to build a fuzzy decision tree from a collection of fuzzy data and an inference procedure for fuzzy decision tree to classify new fuzzy data. It also presents some experiment results to show the applicability of the proposed method.