Research on Classification Model based on Neighborhood Rough Set and Evidence Theory

Abstract

The forward greedy numerical attribute reduction algorithm based on the neighborhood rough set is used to reduce the continuous numerical evaluation metrics of the health condition of complex equipment. This eliminates the risk of data loss and the additional processing time, due to avoiding discretization of continuous numerical evaluation metrics. Furthermore, the reduced evaluation decision table is processed to construct the basic probability assignment function (BBAS). Finally, multiple evaluation metrics are fused by Dempster’s rule of combination to get the health condition grade, and the relationship between evaluation metrics and health condition grade is mined further. The theoretical analysis and experimental results show that the proposed model is effective and efficient for classification.