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 1 and 2 of fuzzy number-valued attribute decision tree. Heuristic algorithm 1 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 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 comparing the algorithm 2 closes to the practice from the opinion of making strategy and is effective to deal with fuzzy information.