A Novel Reduction Algorithm for Decision Table with Continuous Attributes
Decision table is an important tool for knowledge acquisition in rough set theory. In order to reduce the fuzziness and randomness of the decision table with continuous attributes, a novel reduction algorithm based on grey relational degree is proposed. Firstly, the decision table is converted to the same domain. Then, the grey relational matrix is constructed to describe the equivalence relations between samples. Finally, an improving dynamic clustering method is adopted to extract the coarser granularity of the sample, which can automatically cluster the similar samples. The experiment shows that the reduction decision table has gotten roughly the same recognition rate with less than one-tenth the size of the original condition. Thereby it significantly reduces the knowledge acquisition time for rough set.
rough set decision table grey relational degree grey relational analysis
Jin Dai Xin Liu Feng Hu Yi Yan
College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065
国内会议
武汉
英文
433-440
2016-04-22（万方平台首次上网日期，不代表论文的发表时间）