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原文摘要

Multi-view multi-label classification (MVMLC) aims to utilize both consensus and complementarity information to predict potentially relevant labels for samples. Existing MVMLC approaches typically focus on either feature-level fusion, which integrates complementary features for more expressive representations, or decision-level fusion, which aggregates view-specific predictions to exploit label supervision more effectively. In fact, relying solely on feature-level fusion often underutilizes label information and limits discriminability of learned representations, whereas pure decision-level fusion pays insufficient attention to view representation expressiveness and thus constrains classification performance. To address these limitations, we propose DF^2-VB, a dual-level fusion framework that jointly exploits complementary strengths to mitigate their respective weaknesses by integrating feature- and decision-level fusion. At the feature level, a Fuzzy Dynamic Fusion (FDF) module maps consensus features into a more compatible fuzzy feature space, where essential features are identified and redundant features are suppressed to further fuse an expressive consensus representation and boost view-specific predictions for decision-level fusion. At the decision level, a View-specific Boosting (VB) strategy adaptively measures the importance of samples and view-specific predictions to strengthen the utilization of supervision for facilitating the discriminability in feature-level fusion. Complementarily, FDF and VB jointly reinforce the model expressiveness and discriminability for reliable predictions. Extensive experiments on multiple public datasets verify the superiority of our strategy over advanced MVMLC models.