Comparison between the induction learning algorithm of fuzzy number-valued decision tree

From the value of attributes, evaluation functions, stop-criterion of the leaf node and the matching rules used to test examples, this paper discusses the difference and similarities between the induction learning algorithm I and 2 of fuzzy number-valued attribute decision tree. Heuristic algorithm I is 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 and offers a rapid matching speed. Heuristic algorithm 2 is based on the fuzz,v information entropy minimization heuristic, this algorithm is used to choose the test attribute and to construct a fuzz), Bi-branches decision tree with comparison extent. By comparing the algorithm 2 closes to the practice from the opinion of making strategy and is effective to deal with fuzzy information.