Three-way decision with neighborhood rough sets (3WDNRS) is adept at addressing uncertain
problems involving continuous data by configuring the neighborhood radius. However, …

In the context of recent advances in Graph Convolutional Networks (GCNs) for semi-supervised
learning, a significant highlight is the potential of Graph Contrastive Learning (GCL). …

As an extension of Pawlak’s rough sets, rough fuzzy sets is proposed to deal with the target
concept which is typically fuzzy or uncertain. It is worthwhile to introduce cost-sensitive …

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

Graph convolutional network has shown excellent performance in multi-view classification.
Currently, to output a fused node embedding representation in multi-view scenarios, existing …

The "Taihua Group" is a collective term for a series of old terranes scattered along the southern
margin of the North China Craton. The timing of formation and thermal overprinting of the …

Different from classical rough set, Multigranulation Rough Set (MGRS) is frequently designed
for approximating target through using multiple results of information granulation. Presently…

Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In
the previous work, an algorithm based on rough set theory (RST) called KGRSCC was …

Feature selection is an effective data pre-processing technique that aims to select useful
features. This technique has been widely applied in machine learning and data mining to …

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 …