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Volume 11 Issue 11
Nov.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(11): 2204-2219
Y. Lin, Z. Yu, K. Yang, Z. Fan, and  C. L. P. Chen,  “Boosting adaptive weighted broad learning system for multi-label learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2204–2219, Nov. 2024. doi: 10.1109/JAS.2024.124557
Citation: Y. Lin, Z. Yu, K. Yang, Z. Fan, and  C. L. P. Chen,  “Boosting adaptive weighted broad learning system for multi-label learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2204–2219, Nov. 2024. doi: 10.1109/JAS.2024.124557
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Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

doi: 10.1109/JAS.2024.124557
Funds:  This work was supported in part by the National Key R&D Program of China (2023YFA1011601), the Major Key Project of PCL, China (PCL2023AS7-1), in part by the National Natural Science Foundation of China (U21A20478, 62106224, 92267203), in part by the Science and Technology Major Project of Guangzhou (202007030006), in part by the Major Key Project of PCL (PCL2021A09), and in part by the Guangzhou Science and Technology Plan Project (2024A04J3749)
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    Abstract

  • Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system (MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system (MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

     

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    Highlights

    • Aiming at the serious multi-label imbalance problem, this paper innovatively proposes a multi-label weighted broad learning system (MLW-BLS), which alleviates the multi-label imbalance problem from the perspective of label imbalance weighting and label correlation mining
    • Furthermore, this paper proposes the multi-label adaptive weighted broad learning system (MLAW-BLS) to adaptively adjust corresponding label weights and values of MLW-BLS to construct an efficient imbalanced classifier set, which provides a novel idea to overcome the multi-label imbalance problem
    • Extensive comparative experiments are conducted on 30 datasets with 4 metrics to evaluate the effectiveness of MLAW-BLS compared with 7 mainstream algorithms. The results show that MLAW-BLS is superior to other state-of-the-art methods. Ablation experiments on 12 datasets fully verify the effectiveness of each module of MLAW-BLS

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