JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 23-34.doi: 10.6040/j.issn.1671-9352.7.2023.082

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Global and local relationships based on multi-label classification algorithm with label-specific features

ZHANG Shandan1, WENG Wei1,2*, XIE Xiaozhu1, WEI Bowen1, WANG Jinbo3, WEN Juan3   

  1. 1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China;
    2. Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen 361024, Fujian, China;
    3. School of Economics, Xiamen University, Xiamen 361005, Fujian, China
  • Published:2024-05-09

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Abstract: To address the problem of neglecting the second-order label relation in the local label correlation, we propose a new algorithm called global and local relationships based on multi-label classification algorithm with label-specific features(LFGML). Specifically, the label-specific features are firstly obtained through the perspective of global relations, then the neighbourhood information of each instance is calculated using the weighted average method. The second-order label relationship in the local relationship are extracted using Jaccard similarity. The LFGML algorithm is tested on ten multi-label datasets: Genbase, Medical, Arts, Health, Flags, Cal500, Yeast, Image, Education and Emotions. The results demonstrate that our proposed algorithm outperforms other comparison algorithms in multi-label classification.

Key words: multi-label learning, global label correlation, local label correlation, label-specific feature, optimization

CLC Number: 

  • TP181
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