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| Evidence-Theory-Based Optimal Scale Combinations in Generalized Multi-scale Covering Decision Systems |
| WANG Jinbo1,2, WU Weizhi1,2 |
1. School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022;
2. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhou-shan 316022 |
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Abstract Multi-scale data analysis is a hot research direction in the field of granular computing. It simulates the mode of human thinking to establish effective computation models for dealing with multi-level complex data and information. A critical problem in multi-scale data analysis is to select a suitable sub-system from a given system for final classification or decision, and the combination of scale level of each attribute corresponding to the sub-system is called an optimal scale combination of the system. To solve the problem of knowledge acquisition in generalized multi-scale covering decision systems, scale combinations are firstly characterized by belief and plausibility functions in consistent generalized multi-scale covering decision systems. Then, the concepts of seven types of optimal scale combinations in inconsistent generalized multi-scale covering decision systems are defined and their relationships are clarified. It is showed that there are actually four different types of optimal scale combinations. Moreover, it is illuminated that belief and plausibility functions can be applied to characterize lower-approximation optimal scale combinations and upper-approximation optimal scale combinations in inconsistent generalized multi-scale covering decision systems, respectively. Finally, it is illustrated that the proposed methods can be applied to the optimal scale combination selection in incomplete generalized multi-scale decision systems and generalized multi-scale set-valued decision systems, respectively.
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Received: 01 November 2021
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| Fund:Natural Science Foundation of China(No.61976194,41631179,62076221) |
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Corresponding Authors:
WU Weizhi, Ph.D., professor. His research interests include rough set, granular computing, data mining and artificial intelligence.
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| About author:: WANG Jinbo, master student. His research interests include rough set and granular computing. |
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2022, Vol. 35 
