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 …

Uncertainty measures exhibit algebraic and informational perspectives, and the two-view
measure integration facilitates feature selections in classification learning. According to …

Feature selection is related to information processing, and its measurement and algorithm
use various intelligent methodologies, such as neighborhood rough sets (NRSs). At present …

Feature selections facilitate classification learning in various data environments. Aiming at
interval-valued decision systems (IVDSs), feature selections rely on information measures …

Multi-label feature selection corresponds to pattern recognition and knowledge mining, and
its application has been expanded to different scenarios. As an excellent processing …

Mutual information is fundamental for feature selection, and relevant conditional and joint
mutual fuzzy granularities (MFGs) characterize feature correlation and redundancy in fuzzy …

Attribute reductions eliminate redundant information to become valuable in data reasoning.
In the data context of interval-set decision systems (ISDSs), attribute reductions rely on …

Multi-granularity rough sets facilitate knowledge-based granular computing, and their
compromised models (called CMGRSs) outperform classical optimistic and pessimistic …

Feature selection facilitates classification learning and can resort to uncertainty
measurement of rough set theory. By fuzzy neighborhood rough sets, the fuzzy …