Abstract: Decision tree is a popular data mining method, and it is a crucial problem to select expanded attributes in the induction of decision tree. I}he uncertainty of each cut of each continuous valued attributes needs to be measured during the selection of expanded attributes for induction of decision tree based on discretion method, and the computational time complexity is very high. In order to deal with this problem, a method of induction of decision tree for continuous- valued attributes based on tolerance rough sets technique was proposed. "hhe method consists of three stages. First ex- panded attributes are selected with tolerance rough sets technique, and then the optimal cut of the expanded attribute is found, and the sample set is partitioned by the optimal cut, finally the decision tree can be generated recursively. We ana- lysed the computational time complexity of the algorithm in theory and conducted some experiments on multiple data- base. The experimental results and the statistical analysis of the results demonstrate that the proposed method outper- forms other related methods in terms of computational complexity and classification accuracy.

Key words: Tolerance rough sets,Decision trees,Expanded attributes,Cuts,Statistical analysis