关键词: 粗糙集, 属性约简, 互补熵, 正域

Abstract: Attribute reduction, as a special approach for feature selection, is a key concept in rough set theory. The positive-region reduction approach is a kind of common reduction approach, which is of greedy and forward search type. These approaches keep adding one attribute with high significance into a pool during each iteration until positive-region no longer changes. In this paper, by analyzing changes of complementary conditional entropy varying with partition, four situations about changes of positive-region and entropy induced by adding a new attribute to the candidate attribute set are introduced. Then, a positive-region reduction algorithm based on complementary entropy is developed. Experimental results show that compared with the traditional positive-
region reduction algorithm, the proposed algorithm can find a reduction including fewer attributes and possessing almost same decision performance in a significantly shorter time.

Key words: rough set, attribute reduction, complement entropy, positive region