5 papers
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…
Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
Wanfu Gao, Hanlin Pan, Qingqi Han +1
The "Curse of dimensionality" is prevalent across various data patterns, which increases the risk of model overfitting and leads to a decline in model classification performance. H…
Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
Wanfu Gao, Jun Gao, Qingqi Han +2
The rapid growth in feature dimension may introduce implicit associations between features and labels in multi-label datasets, making the relationships between features and labels…
Dual-Agent Reinforcement Learning for Automated Feature Generation
Wanfu Gao, Zengyao Man, Hanlin Pan +1
Feature generation involves creating new features from raw data to capture complex relationships among the original features, improving model robustness and machine learning perfor…
Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection
Hanlin Pan, Kunpeng Liu, Wanfu Gao
The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label amb…