In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …

Information theoretical-based methods have attracted a great attention in recent years and
gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the …

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 multi-label learning, feature selection is a topical issue for addressing high-dimension
data. However, most of existing methods adopt imperfect labels to perform feature selection …

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 …

Feature selection is an effective way to improve the model learning performance while being
a challenging problem in the Partial Multi-label Learning (PML). Different from multi-label …

Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can
effectively learn node representations via aggregating neighbors based on the relation …

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 …