Abstract： Classification is a core problem in data mining and machinery learning. Nowadays data are usually multi-dimensional, which contain so many properties. However, some properties may be redundant and interfere with the correct and clear decisions. In order to obtain the maximum classification precision, it is so important to select the proper decision tree in the process of classification of decision tree. The common methods of decision property selection include relevant analytical method, componential analysis methods and so on. This paper analyzed the method of property reduction about rough theory and applied it to decision tree, which can produce multiple choice problems of relative reduction.
刘远峰 杨碧华. 基于粗糙理论的属性约简在决策树中应用[J]. , 2010, 1(09): 0-0.
Liu Yuanfeng Yang Bihua. Application of Property Reduction in Decision Tree Based on Rough Theory. , 2010, 1(09): 0-0.
2010, Vol. 1 