1993 Volume 113 Issue 7 Pages 488-494
ID 3 algorithm can automatically acquire a decision tree from a set of training examples. However, ID 3 can only deal with the distinct data values. This paper presents a fuzzy decision tree which expresses some fuzzy classification rules, and an algorithm to induce a fuzzy decision tree from the training examples including numerical or fuzzy data values. This new algorithm, called IDF, has a labeling procedure in each decision tree expanding step to make effective fuzzy classification items. These items are used to do fuzzy decisions in the branch nodes. Fuzzy decision tree can also give some classification results with certainty ratios. The authors examined and exemplified the efficiency of this algorithm by some numerical experiments.