15 papers
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…
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…
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…
The Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks
Wanfu Gao, Zebin He, Jun Gao
Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dep…
Combining LLM Semantic Reasoning with GNN Structural Modeling for Multi-View Multi-Label Feature Selection
Zhiqi Chen, Yuzhou Liu, Jiarui Liu +1
Multi-view multi-label feature selection aims to identify informative features from heterogeneous views, where each sample is associated with multiple interdependent labels. This p…
Redundancy-optimized Multi-head Attention Networks for Multi-View Multi-Label Feature Selection
Yuzhou Liu, Jiarui Liu, Wanfu Gao
Multi-view multi-label data offers richer perspectives for artificial intelligence, but simultaneously presents significant challenges for feature selection due to the inherent com…