A unified incremental updating framework of attribute reduction for two-dimensionally time-evolving data

In the open-world environment, the incremental updating approaches to attribute reduction based on rough sets are efficient and effective to evaluate and search an optimal subset of attributes from two-dimensionally time-evolving data, which can be interpreted as the complex changes of dynamic data, i.e., four types of combinations induced by the insertion/deletion of objects and the addition/remove of attributes. To avoid the time-consuming and repetitive computation from scratch in such dynamic data, this paper mainly focuses on constructing a unified incremental framework to attribute reduction by the matrix-based accelerated updating strategies. We systematically discuss and present a series of incremental updating mechanisms and algorithms of approximation quality in the neighborhood-based probabilistic rough sets. Besides, a unified framework of dynamic attribute reduction in four situations of changes is proposed to develop the performance of updating reduct. Finally, we report the comparative experiments between the nonincremental and incremental algorithms of reduct to demonstrate the feasibility and efficiency of proposed approaches. (C) 2022 Elsevier Inc. All rights reserved.