Feature selection is a crucial preprocessing step in data analytics and machine learning.
Classical feature selection algorithms select features based on the correlations between …

Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Causal feature selection has attracted much attention in recent years, since it has better
robustness than the traditional feature selection. Existing causal feature selection algorithms …

Multi-label feature selection has received considerable attentions during the past decade.
However, existing algorithms do not attempt to uncover the underlying causal mechanism …

Abstract Machine learning has achieved impressive results in biomedical data analysis. To
cope with high-dimensional data, variable selection is proposed to identify patterns in the …

Markov boundary (MB) has been widely studied in single-target scenarios. Relatively few
works focus on the MB discovery for variable set due to the complex variable relationships …

Recent years have witnessed the proliferation of techniques for streaming data mining to
meet the demands of many real-time systems, where high-dimensional streaming data are …

In a Bayesian network (BN), the Markov boundary (MB) presents the local causal structure
around a target. Due to the interpretability and robustness, it has been widely applied to …

Directed acyclic graph (DAG) learning plays a key role in causal discovery and many
machine learning tasks. Learning a DAG from high-dimensional data always faces …

Theoretically, the Markov boundary (MB) is the optimal solution for feature selection.
However, existing MB learning algorithms often fail to identify some critical features in real …