An Attribute Reduction Algorithm Based on Conditional Entropy and Frequency of Attributes
The attribute reduction and relative attribute reduction were discussed in this paper. They are the core of KDD. The information view and the algebra view of rough set theory were combined and a novel attribute reduction algorithm was proposed. In the algorithm, the core attribute set which is the initial candidate reduction set is obtained from the discernibility matrix. The frequency of attributes, got from the filtered discernibility matrix, is used as the heuristic information of attributes selection. The algorithms terminal condition is realized by the conditional entropy. Taking the climatic factor reduction in load forecasting as an example, it has proved that the algorithm requires less computation, has high efficiency and can reduce the redundant attribute in the relative reduction set to a certain extent.
Cuiru WANG Fangfang OU
School of Computer Science and Technology of North China Electric Power University, Baoding 071003, China
国际会议
长沙
英文
752-756
2008-10-20（万方平台首次上网日期，不代表论文的发表时间）