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cs.LG2026
SEHFS: Structural Entropy-Guided High-Order Correlation Learning for Multi-View Multi-Label Feature Selection
Cheng Peng, Yonghao Li, Wanfu Gao +2
In recent years, multi-view multi-label learning (MVML) has attracted extensive attention due to its close alignment to real-world scenarios. Information-theoretic methods have gai…
cs.LG2026
Ca-MCF: Category-level Multi-label Causal Feature selection
Wanfu Gao, Yanan Wang, Yonghao Li
Multi-label causal feature selection has attracted extensive attention in recent years. However, current methods primarily operate at the label level, treating each label variable…
cs.LG2026
From Ambiguity to Action: A POMDP Perspective on Partial Multi-Label Ambiguity and Its Horizon-One Resolution
Hanlin Pan, Yuhao Tang, Wanfu Gao
In partial multi-label learning (PML), the true labels are unobserved, which makes label disambiguation important but difficult. A key challenge is that ambiguous candidate labels…