Feature selection (FS) aims to eliminate redundant features and choose the informative
ones. Since labeled data are not always easily available and abundant unlabeled data are …

Feature selection becomes inevitable owing to a rapid increase in digital technology which
permits the generation of high dimensional data in a large quantity within a short time …

Graph convolutional networks (GCNs) have become one of the important technologies for
solving graph structured data problems. GCNs utilize convolutional networks to learn node …

Feature interaction is a vital aspect of Machine Learning (ML) algorithms, and gaining a
deep understanding of these interactions can significantly enhance model performance …

The nature of human movement and non-movement behaviors is complex and multifaceted,
making their study complicated and challenging. Thanks to the availability of wearable …

Meta-heuristic algorithms have been extensively utilized in feature selection tasks because
they can obtain the global optimal solution. However, the meta-heuristic algorithm will take …

Partial label learning is a multi-class classification issue in which each training instance is
associated with a set of candidate labels. Feature selection is an effective method to improve …

As combinations of neighborhood rough sets and multigranulation rough sets (MRSs),
optimistic and pessimistic neighborhood MRSs can handle complex information systems …

In the realm of machine learning, feature selection emerges as a prevalent data
preprocessing technique, playing a crucial role in enhancing model performance across …

As a very prominent research application of the theory of rough sets, attribute reduction
technique has made significant strides in a lot of fields, including decision making, granular …