Feature selection is a significant preprocessing technique for data mining, which can
promote the accuracy of data classification and shrink feature space by eliminating …

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

Screening biomolecular markers from high-dimensional biological data is one of the long-
standing tasks for biomedical translational research. With its advantages in both feature …

As a reliable and valid tool for analyzing uncertain information, fuzzy rough set theory has
attracted widespread concern in feature selection. However, the performance of fuzzy rough …

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

Feature selection based on neighborhood rough sets (NRSs) has become a popular area of
research in data mining. However, the limitation that NRSs inherently ignore the differences …

The performance of the neighborhood rough set model in feature selection is limited by
nonobjective parameter selection method, the uncertainty measures considered only from a …

In fuzzy neighborhood rough sets (FNRSs), uncertainty measurement performs mainly
classification-hierarchical and multiplication-simple fusion, so the corresponding feature …

The neighborhood rough set model (NRS) has been widely applied to study feature
selection. Nevertheless, the dependency, as a significant feature evaluation function in NRS …

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