Multi-label feature selection, which addresses the challenge of high dimensionality in multi-
label learning, has wide applicability in pattern recognition, machine learning, and related …

Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

With the continuous evolution of artificial intelligence and sensor technology, there is a
growing accumulation of unlabeled data. Uncovering valuable insights from this data has …

Fuzzy rough set is a highly effective mathematical method for feature selection, which offers
clear interpretability without expert knowledge. However, most of fuzzy-rough feature …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Overlap functions, which can be characterized as a type of non-associative binary
aggregation operators, have emerged as one of the most extensively utilized aggregation …

Fuzzy rough set model is a powerful tool for handling attribute reduction tasks for complex
data. While the fuzzy rough set model commonly employs fuzzy information entropy to …

Granular structures are mathematical representations of knowledge used in granular
computing. As a new type of granular structure, fuzzy β-covering has attracted widespread …

Fuzzy information-theoretic metrics have been demonstrated to be effective in evaluating
feature relevance and redundancy in both categorical and numerical feature selection tasks …

How to evaluate the reliability and relative weight of the evidence play a key role in
improving the performance of the model using Dempster–Shafer (D–S) evidence theory. A …