Dynamic Algorithm for Computing Attribute Reduction Based on Information Granularity

Dynamic attribute reduction is one of the important issues in rough set theory.A dynamic attribute reduction model based on information granularity was constructed in dynamic decision table,and an incremental approach for computing information granularity was discussed in detail when some new attribute set is added into decision table.On this basis,a dynamic attribute reduction algorithm was proposed by using information granularity as the heuristic information.The proposed algorithm can use attribute reduction and information granularity of original decision table,which can effectively reduce the computational complexity,so that the attribute reduction has better inheritance.Finally,the example and experimental comparison indicate the feasibility and validity of the proposed algorithm.