The Sensitivity Of The Parameter In Building Fuzzy Decision Tree

Induction learning of decision tree based on ID3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge. With the deeper research of inductive learning, it can't meet the automatic acquisition of non-crisp knowledge because of its crisp description. It appears to be very important to research inductive learning in uncertainty condition and therefore the fuzzy extension of traditional ID3-fuzzy ID3 is proposed. In building fuzzy decision tree, each expanded attribute can't classify the class label clearly like decision tree, but the cases covered with the attribute-values have some overlap. So the entire process of building trees is based on a significant level a, the import of a can reduce such overlap in some degree, decrease the uncertainty of classification and improve classification result. But the value of a is given directly by domain expert based on experience or requirement, which depend on expert's knowledge excessively, therefore do not gain the best classification result possibly.By analyzing expression between a and fuzzy entropy from the view of analytics, this paper analyses the relationship of between a and fuzzy entropy and the changing trend of fuzzy entropy function with the increase of a, then discusses the sensitivity of the parameter a to classification result such as total nodes, rule number, classification accuracy of fuzzy decision tree, proposes an experimental method of obtaining optimal a , It is proved by experiment that the optimal value a obtained by this method can make the classification result of fuzzy decision tree best, and therefore provides the academic evidence of selecting parameter a in order to gain the best classification result.