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

This paper summarizes the related works about feature selection via the induction of Markov
blanket which can be traced back to 1996, and the concept of Markov blanket itself firstly …

Classification of microarray data plays a significant role in the diagnosis and prediction of
cancer. However, its high-dimensionality (> tens of thousands) compared to the number of …

Discovering causal relationships from observational data is a crucial problem and it has
applications in many research areas. The PC algorithm is the state-of-the-art constraint …

This article presents our vision on the need of developing new managing technologies to
harness distributed “computing continuum” systems. These systems are concurrently …

Causal feature selection has achieved much attention in recent years, which discovers a
Markov boundary (MB) of the class attribute. The MB of the class attribute implies local …

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 …

In this article, we aim to develop a unified view of causal and non-causal feature selection
methods. The unified view will fill in the gap in the research of the relation between the two …

The discovery of Markov blanket (MB) for feature selection has attracted much attention in
recent years, since the MB of the class attribute is the optimal feature subset for feature …

The Markov blanket (MB), a key concept in a Bayesian network (BN), is essential for large-
scale BN structure learning and optimal feature selection. Many MB discovery algorithms …