In practical applications, there exist lots of ordered information systems (OISs). In the process of dealing with OISs, dominant preference, which plays a significant role in decision making, should be taken into consideration. With the increasing of data capacity, OISs often evolve with time. In order to extract updated knowledge from evolving ordered data, we have to elaborate computation efforts to re-calculate entire data, which consumes a significant computational cost. Therefore, the computational efficiency is extremely low. In response to this challenge, matrix-based dynamic dominance-based multigranulation rough sets (DMGRSs) approaches, which can improve computational efficiency for updating knowledge, are explored to update multigranulation approximations in dynamic ordered information systems with evolving data. To begin with, we present a matrix representation of dominance-based multigranulation approximations according to the dominant relation matrix and relevant column vectors of each granular structure. Afterwards, the incremental strategies to update dominance-based multigranulation approximations in OISs are proposed when adding or deleting objects. Furthermore, the corresponding dynamic algorithms, which avoid some unnecessary calculations, are explored in DMGRSs. Finally, extensive experiments carried out on nine UCI data sets indicate that the explored dynamic algorithms can achieve promising performance.

在实际应用中，存在许多有序信息系统（OIS）。在处理OIS的过程中，应考虑在决策中起重要作用的主导偏好。随着数据容量的增加，OIS通常会随着时间而发展。为了从不断发展的有序数据中提取更新的知识，我们必须精心设计计算工作以重新计算整个数据，这会消耗大量的计算成本。因此，计算效率极低。为了应对这一挑战，探索了可提高知识更新效率的基于矩阵的基于动态优势的多粒度粗糙集（DMGRS）方法，以利用动态数据更新动态有序信息系统中的多粒度近似。首先，我们根据主导关系矩阵和每个粒度结构的相关列向量，给出了基于优势的多粒度近似的矩阵表示。然后，提出了在添加或删除对象时更新OIS中基于优势的多粒度近似的增量策略。此外，在DMGRS中探索了避免某些不必要计算的相应动态算法。最后，对9个UCI数据集进行的广泛实验表明，探索的动态算法可以实现有希望的性能。在增加或删除对象时，提出了在OIS中更新基于优势的多粒度近似的增量策略。此外，在DMGRS中探索了避免某些不必要计算的相应动态算法。最后，对9个UCI数据集进行的广泛实验表明，探索的动态算法可以实现有希望的性能。提出了在添加或删除对象时更新OIS中基于优势的多粒度近似的增量策略。此外，在DMGRS中探索了避免某些不必要计算的相应动态算法。最后，对9个UCI数据集进行的广泛实验表明，探索的动态算法可以实现有希望的性能。