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 algorithmpsilas 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.