关键词: 近似质量, 属性约简, 条件熵, 邻域粗糙集

Abstract: In traditional heuristic algorithm to compute reduction, one and only one measurement is used to construct the constraint. Therefore, the derived reduction may not always meet the constraints based on multi-measurement. Moreover, most of the definitions of attribute reductions are given by considering all decision classes, while ignoring the changes in the metrics corresponding to different decision categories before and after reduction. To solve these problems, an attribute reduction strategy for local multi-constraint is proposed, which aims to make each decision class meet the constraints based on multi-measurement. By using neighborhood rough set, the traditional strategy is compared with the local multi-constraint strategy in computing reductions on UCI data sets. The approximate quality and conditional entropy are selected as the measurements used in multi-constraint. The experimental results show that the local multi-constraint based attribute reduction can significantly reduce the conditional entropy while it meets the constraint of the approximate quality. The effectiveness of local multi-constraint strategy is then displayed.

Key words: approximate quality, attribute reduction, conditional entropy, neighborhood rough set