Multiview sequential three-way decisions based on partition order product space

In granular computing, a set of attributes (features) is often selected as a view to describe objects from a particular angle. In each view, objects can be further described from different levels of granularities (abstraction), and each granularity determines a level. Multiview and multilevel are two basic principles of granular computing, which render a solution more comprehensive and reasonable. From the viewpoint of granular computing, existing three-way decisions cannot effectively combine multiview and multilevel to make decisions. As a new granular computing model, the partition order product space solves a problem from multiple views and at multiple levels in each view, which follows the principles of multiview and multilevel. In this paper, we discuss three-way decisions based on partition order product space. First, we propose two search algorithms: depth-first and breadth first searching algorithms, to obtain a solution for problem solving in partition order product space. Second, we introduce two fusion strategies to fuse multiple one-level views: optimistic fusion method and pessimistic fusion method. Consequently, based on two search algorithms and two fusion strategies, we propose four multiview sequential three-way decisions, which can simultaneously make decisions from multiple views and multiple levels. Experimental results demonstrate the effectiveness of the proposed models. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper discusses the importance of multiview and multilevel in granular computing, and introduces a new partition order product space model. It proposes search algorithms and fusion strategies for solving three-way decisions from multiple views and multiple levels.