Induction of decision tree with fuzzy number-valued attribute

To the learning problems of the triangle type fuzzy number-valued attribute, we present an algorithm regarding the fuzzy number-valued attribute based on the fuzzy information entropy minimization heuristic, this algorithm is used to choose the test attribute and to construct a fuzzy bi-branches decision tree with comparison extent. By defining comparison extent between a real and a fuzzy number, we can avoid the more lost of information. From the opinion of making strategy, the given algorithm closes to the practice and is effective to deal with fuzzy information.

[1]  Xi-Zhao Wang,et al.  Induction of bi-branches decision tree with fuzzy number-value attribute , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[2]  Xizhao Wang,et al.  On the optimization of fuzzy decision trees , 2000, Fuzzy Sets Syst..

[3]  Usama M. Fayyad,et al.  What Should Be Minimized in a Decision Tree? , 1990, AAAI.

[4]  M. Shaw,et al.  Induction of fuzzy decision trees , 1995 .

[5]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[6]  Jie Cheng,et al.  Improved Decision Trees: A Generalized Version of ID3 , 1988, ML.