1 citations · 1 across the 16 of their papers we have counts for
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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…
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…
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…
Two-Stage Feature Generation with Transformer and Reinforcement Learning
Wanfu Gao, Zengyao Man, Zebin He +3
Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new feat…
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…
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
Pingting Hao, Kunpeng Liu, Wanfu Gao
In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative…
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…
TFWT: Tabular Feature Weighting with Transformer
Xinhao Zhang, Zaitian Wang, Lu Jiang +3
In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume…