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

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 …

In this study, we investigate a challenging problem, namely, robust domain adaptation,
where data from only a single well-labeled source domain are available in the training …

We introduce a new local-to-global structure learning algorithm, called graph growing
structure learning (GGSL), to learn Bayesian network (BN) structures. GGSL starts at a …

Recent years have witnessed the proliferation of causal learning techniques, aimed at
extracting the abundant causal relationships embedded within observational data. In many …

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 …

In Bayesian network structure learning, the quality of the directed graph learned by the
constraint-based approaches can be greatly affected by the order of choosing variable pairs …

Earlier studies have shown that classification accuracies of Bayesian networks (BNs)
obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the …

Abstract Knowledge tracing predicts student knowledge acquisition states during learning.
Traditional knowledge tracing methods suffer from poor prediction performance; however …