[1]XU Yi,ZHANG Jie.Multi-scale decision model based on partition order product space[J].CAAI Transactions on Intelligent Systems,2024,19(6):1528-1538.[doi:10.11992/tis.202306026]
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Multi-scale decision model based on partition order product space

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1528-1538 Column: 学术论文—人工智能基础 Public date: 2024-12-05
Title:
Multi-scale decision model based on partition order product space
Author(s):
XU Yi1; 2;  ZHANG Jie2
1. School of Computer Science and Technology, Anhui University, Hefei 230601, China;
2. Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Hefei 230601, China
Keywords:
granular computing; rough set; multi-scale decision system; partition order product space; multilevel; multiview; lattice structure; optimal problem solving level
CLC:
TP18
DOI:
10.11992/tis.202306026
Abstract:
Knowledge acquisition in multiscale decision systems is an important research problem. Existing studies on multiscale decision systems only typically address multiple scales of condition and decision attributes, but they often overlook scenarios where condition attributes have multiple views. As a new granular computing model, the partition order product space simultaneously considers multiple levels and views. Therefore, this paper uses the partition order product space to describe and solve multiscale decision problems and establishes a multiscale decision model based on this space, which is referred to as the partition order multiscale decision system. First, the study proposes a partition order multiscale decision system based on the partition order product space, which can describe multiscale decision problems from multiple views. Second, two different lattice structures within the problem solution space of the partition order multiscale decision system are provided. Third, two optimal problem-solving level selection algorithms are introduced for the two different lattice structures to address the multiscale decision problem from multiple views. Finally, the effectiveness of the proposed model and algorithms is verified through experiments.
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