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Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning
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DOI：10.1109/JAS.2024.124557.png)
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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.
Keyword:
Learning systems
Adaptation models
Correlation
Adaptive systems
Annotations
Boosting
Classification algorithms
Broad learning system
label correlation mining
label imbalance weighting
multi-label imbalance
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19.2
论文数:
1.4K
被引数:
1.1W
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