Compressed binary discernibility matrix based incremental attribute reduction algorithm for group dynamic data

摘要

The datasets in real-world applications often vary dynamically over time. Moreover, datasets often expand by introducing a group of data in many cases rather than a single object one by one. The traditional incremental attribute reduction approaches for a single dynamic object may not be applied to such cases. Focusing on this issue, a compressed binary discernibility matrix is introduced and an incremental attribute reduction algorithm for group dynamic data is developed. The single dynamic object and the group dynamic objects are both considered in this algorithm. According to the dynamic data is a single object or a group of objects, different branches can be chosen to update the compressed binary discernibility matrix. Thereafter, the incremental reduction result can be obtained based on the updated compressed binary discernibility matrix. The validity of this algorithm is demonstrated by simulation and experimental analysis. (C) 2019 Elsevier B.V. All rights reserved.