A New Partition Criterion for Fuzzy Decision Tree Algorithm

Decision trees represent a simple and powerful method of induction from labeled instances. Fuzzy decision tree is the generalization of decision tree in fuzzy environment. The knowledge represented by fuzzy decision tree is more natural to the way of human thinking, but it´s preprocess and tree-constructing are much costly. In this paper, we propose a modified fuzzy decision tree model (MFD). Entropy of multi-valued and continuous-valued attributes is both computed with fuzzy theory after fuzzification, while entropy of other attributes is dealt with General Shannon method. Experiment results suggest that the proposed model is more effective and efficient and can leads to comprehensible decision trees.