Affiliations: The Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China | College of Mathematics & Physics, Chongqing University of Posts and Telecommunications, Chongqing, China
Note: [] Corresponding author. Qinghua Zhang, College of Mathematics & Physics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Tel.: +86 13002361553; E-mail: zhangqh@cqupt.eud.cn
Abstract: Rough set is a mathematical tool proposed by professor Pawlak to deal with uncertain knowledge. Attribute reduction is one of the core contents of rough set theory when people acquire knowledge from an information system. The existing reduction algorithms are often based on a kind of attribute importance, without considering the application information such as the costs, users' preferences, etc. Firstly, through the analysis of existing attribute reduction algorithms based on the attribute importance, the weighted attribute importance considering users' requirements is proposed. Secondly, based on the weighted attribute importance, a new attribute reduction algorithm is presented, and the algorithm's completeness is proved in detail. Finally, experiments on the proposed algorithm have been completed, and the experiment results show that compared with the existing algorithms, the reduction results of the proposed algorithm are more coincident with the actual requirements of users.
