Set-Valued Fuzzy Granular Ball Rough Set Model and Attribute Reduction Algorithm for Set-Valued Decision Systems
LUO Zhongtuan1, TAN Anhui2, GU Shenming1, WU Weizhi1
1. School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022; 2. School of Mathematical Sciences, Huaqiao University, Quan-zhou 362021
Abstract：As an effective multi-granularity data processing paradigm, granular ball computing(GBC) exhibits significant potential in the fields of complex data analysis and knowledge reduction. However, existing GBC models fail to fully characterize the fuzziness and uncertainty inherent in set-valued attributes, restricting their application in set-valued decision systems. To address this limitation, a set-valued fuzzy granular ball rough set model is proposed and a corresponding attribute reduction algorithm is designed. Firstly, an adaptive granular ball generation algorithm is proposed to enable the dynamic granulation of the data space based on the characteristics of set-valued data. Secondly, fuzzy granular ball tolerance relations and approximation operators are introduced, and their mathematical properties are systematically analyzed and proven. Furthermore, a forward greedy attribute reduction algorithm is constructed based on dependency degree to achieve efficient attribute reduction. Finally, experimental results demonstrate that the proposed algorithm not only obtains more compact attribute reducts but also achieves higher average classification accuracy with CART and SVM classifiers. These results validate its effectiveness and superiority in set-valued decision systems.
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2025, Vol. 38 
