Efficient approaches for maintaining dominance-based multigranulation approximations with incremental granular structures

Abstract

In practical decision making applications, it is computationally time-consuming to maintain multigranulation approximations from scratch in dynamic ordered decision information systems (ODISs) with incremental granular structures consisting of the changing of granular structures by adding granular structures, or by adding an attribute set into each granular structure. The time consumed in the process of maintaining approximations from scratch makes it natural to take into account incremental strategies in order to reduce computational complexity in dynamic multigranulation contexts. To address this challenge, we propose two matrix-based incremental strategies that can dynamically update the lower and upper approximations of each decision class with incremental granular structures in dominance-based multigranulation rough sets (DMGRSs). Moreover, the corresponding incremental algorithms are designed for handling dynamic multi-source ordered data. Ultimately, empirical experiments conducted on UCI data sets depict that the proposed algorithms exhibit a better computational performance compared with the matrix-based static algorithm.