Induction of bi-branches decision tree with fuzzy number-value attribute

This paper presents an algorithm regarding the fuzzy number-valued attribute using the information entropy minimization heuristic. The algorithm gives us a desirable behavior of the information entropy of partitioning. The efficiency of the learning algorithm is improved by analyzing the non-stable cut point and the experiment result shows that the number of leaves in decision tree generation is reduced with the raising of level α. Thus, the scale of decision tree and the recognition rate of classification using the proposed algorithm are improved with the raising of level α. To the unknown-classified sample data, the algorithm offers a rapid matching speed. Finally, the example on medical records that we collected in a hospital shows the utility of the proposed algorithm.