The concept of three-way decision, interpreted and described as thinking, problem solving,
and information processing in “threes”, has been widely studied and applied in machine …

Variable precision (fuzzy) rough sets are interesting generalizations of Pawlak rough sets
and can handle uncertain and imprecise information well due to their error tolerance …

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 …

Hierarchical classification is an important research hotspot in machine learning due to the
widespread existence of data with hierarchical class structures. The existing sequential …

The optimal global feature subset cannot be found easily due to the high cost, and most
swarm intelligence optimization-based feature selection methods are inefficient in handling …

Feature selection benefits machine learning and knowledge acquisition, and it usually
resorts to various intelligent methodologies. Fuzzy rough sets act as a powerful platform of …

Dempster–Shafer evidence theory is widely used in multi-sensor data fusion. However, how
to manage the counterintuitive result generated by the highly conflicting evidence remains …

Feature selection facilitates intelligent information processing, and the unsupervised
learning of feature selection has become important. In terms of unsupervised feature …

Multiattribute group decision-making (MAGDM) is a decision analysis method used to
address decision-making problems that have multiple attributes and multiple decision …

The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm
of rough sets, offering a broader scope of applicability. This approach, deeply rooted in …