7 papers · 1 filter
DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection
Bahareh Golchin, Banafsheh Rekabdar, Kunpeng Liu
Anomaly detection in time series data is important for applications in finance, healthcare, sensor networks, and industrial monitoring. Traditional methods usually struggle with li…
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…