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Efficient approaches for maintaining dominance-based multigranulation approximations with incremental granular structures
International Journal of Approximate Reasoning ( IF 3.9 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.ijar.2020.08.005
Chengxiang Hu , Li Zhang

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.

中文翻译:

使用增量粒度结构保持基于优势的多粒度近似的有效方法

摘要 在实际决策应用中,在动态有序决策信息系统 (ODIS) 中从头开始维护多粒度近似是非常耗时的,该系统具有增量粒度结构,包括通过添加粒度结构或添加属性集来改变粒度结构。进入每个颗粒结构。从头开始维护近似值的过程中消耗的时间使得考虑增量策略以降低动态多粒度上下文中的计算复杂性变得很自然。为了应对这一挑战,我们提出了两种基于矩阵的增量策略,它们可以在基于优势的多粒度粗糙集 (DMGRS) 中使用增量粒度结构动态更新每个决策类的下近似和上近似。此外,还设计了相应的增量算法来处理动态多源有序数据。最终,在 UCI 数据集上进行的实证实验表明,与基于矩阵的静态算法相比,所提出的算法表现出更好的计算性能。
更新日期:2020-11-01
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