An Incremental Updating Algorithm for Attribute Reduction Based on Improved Discernibility Matrix

Rough set theory is a new mathematical tool to deal with imprecise,incomplete and inconsistent data.Attribute reduction is one of important parts researched in rough set theory.Many existing algorithms mainly aim at the case of stationary information system or decision table,very little work has been done in updating of an attribute reduction.Therefore,in this paper,the authors introduce an incremental updating algorithm for attribute reduction based on discernibility matrix in the case of inserting,which only inserts a new row and column,or deletes one row and updates corresponding column when updating the decernibility matrix.After dynamically computing a core,attribute reduction can be effectively updated by utilizing the old attribute reduction.Theoretical analysis shows that the algorithm of this paper is efficient and feasible.