Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (3): 127-136.doi: 10.6040/j.issn.1672-3961.0.2024.322

• Machine Learning & Data Mining • Previous Articles     Next Articles

Feature subset selection for fuzzy multi-scale multi-label data

WANG Chengzhi1,2,3, LIN Guoping1,2,3*, JIANG Liang4, LIN Yidong1,2,3, QIN Yujie1,2,3   

  1. WANG Chengzhi1, 2, 3, LIN Guoping1, 2, 3*, JIANG Liang4, LIN Yidong1, 2, 3, QIN Yujie1, 2, 3(1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363099, Fujian, China;
    2. Fujian Key Laboratory of Data Science and Statistics(Minnan Normal University), Zhangzhou 363099, Fujian, China;
    3. Fujian Key Laboratory of Granular Computing and Applications, Minnan Normal University, Zhangzhou 363099, Fujian, China;
    4. Key Laboratory of Applied Mathematics(Putian University), Putian 351100, Fujian, China
  • Published:2026-06-09

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Abstract: Multi-scale rough sets became one of the research hotspots in the field of granular computing. The classical Wu-Leung model performed well in handling discrete single-label data; However, it was limited in dealing with fuzzy multi-label data. To address this issue, this study drew on the theory of multi-label learning to construct a corresponding information system for fuzzy multi-scale multi-label data, and designed an algorithm that simultaneously achieved optimal scale selection and feature subset selection. Finally, experiments were conducted on seven standard multi-label datasets, and the results demonstrated the effectiveness of the proposed algorithm in feature dimensionality reduction and its stability feasibility.

Key words: multi-scale information system, multi-label learning, fuzzy rough set, feature subset selection

CLC Number: 

  • TP18
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