Incremental rough reduction with stable attribute group

In dynamic and open data environment, how to improve the performance of reduction is of great importance from incremental evaluation of attributes and quick search of attributes. In this paper, by considering both two perspectives, we first combine the incremental technology and the accelerated strategy in attribute reduction. On the one hand, we utilize the stable attribute group generated by DBSCAN to accelerate the process of searching reduction. On the other hand, we propose the matrix-based incremental mechanisms to dynamic attribute reduction when the objects are evolved over time. Moreover, these two methods are fused together in a unified algorithm of reduction. Finally, a series of comparative experiments is conducted to verify the effectiveness of proposed approach from stability, computational cost, and classification accuracy. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

In this paper, a method for improving the performance of attribute reduction in a dynamic data environment is proposed. By combining incremental technology and accelerated reduction strategy, the method utilizes stable attribute groups and matrix-based incremental mechanisms for reduction search and dynamic attribute reduction. Experimental results demonstrate the effectiveness of the proposed method in terms of stability, computational cost, and classification accuracy.