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Multigranulation sequential three-way decisions based on multiple thresholds
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DOI：10.1016/j.ijar.2018.12.007.png)
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Three-way decisions have become a representative of trisecting-and-acting model for uncertainty data analysis. With granular computing point of view, a more reasonable decision-making model should process data from multiview and multilevel. Classical sequential three-way decisions are based on a granular structure with a single threshold under multiple levels of granularity, which make the traditional models incapable of adapting to the case of multiview granular structures with multiple thresholds. To overcome this disadvantage, we propose a generalized multigranulation sequential three-way decision model based on multiple different thresholds. By controlling the number of multiview granular structures satisfying the corresponding two tolerance thresholds, we further adopt the optimistic, pessimistic, variable, weighted and weighted arithmetic mean aggregation strategies to construct five kinds of multigranulation sequential three-way decision models from the quantitative point of view. The corresponding relationships and the uncertainty measures of these multigranulation sequential three-way decisions are discussed. Finally, the experimental results demonstrate that different multigranulation sequential three-way decisions can solve the problem under multiple granular structures and have more flexible fault tolerance under different levels of granularity. This study will enrich and prompt the development of the multigranulation three-way decisions. (C) 2018 Elsevier Inc. All rights reserved.
Keyword:
Granular computing
Multigranulation rough set model
Sequential three-way decisions
Multiple thresholds
Uncertainty measure
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