Dimension Incremental Attribute Reduction Methods Based on Discernibility Matrix
WU Xiao-ying1，WEI Wei(1,2)，CUI Jun-biao1
1（School of Computer &Information Technology,Shanxi University,Taiyuan 030006,China）2（Key Laboratory of Computation Intelligence & Chinese Information Processing,Ministry of Education,Taiyuan 030006,China）
Abstract：Dimension increase is one of the important types of dynamic data.In order to calculate attribute reduction of this kind of data efficiently,this paper put forward two types of dimension incremental attribute reduction methods based on discernibility matrix:one way is to compute new reduction by modifying a decision table′s discernibility matrix;Another way is to compute dimension incremental attribute reduction by updating a compacted decision table′s discernibility matrix.Both methods can obtain the same results with the non-incremental attribute reduction method.They also significantly reduce time-consuming of computing attribute reduction in the case of the dimension of a decision table increasing,and the method based on a compacted decision table is faster.Theoretical analysis and experimental results verify the effectiveness and efficiency of these two proposed algorithms.
2017, Vol. 38 
