Rough set theories are utilized in class-specific feature selection to improve the
classification performance of continuous data while handling data uncertainty. However …

Research in the area of fuzzy–rough set theory, and its application to feature or attribute
selection in particular, has enjoyed much attention in recent years. Indeed, with the growth of …

Feature selection is an important data preprocessing technique, and has been widely
studied in data mining, machine learning, and granular computing. However, very little …

Neighborhood rough set (NRS) is an important tool for granular computing. It can handle
discrete and continuous data without a prior discretization. However, the definitions of NRS …

Neighbourhood rough set theory has proven already, as an efficient tool for knowledge
discovering from heterogeneous data. However, some types of the data are incomplete and …

Neighborhood rough sets are widely used as an effective tool to deal with numerical data.
However, most of the existing neighborhood granulation models cannot well describe the …

In real application domains, acquiring fine-grained data has a higher cost than coarse-
grained data. To achieve the best results at the lowest cost, it is necessary to select an …

In order to solve the problem that the classical fuzzy rough set (FRS) model used for feature
selection is sensitive to noisy information, we propose an effective robust fuzzy rough set …

Multigranulation rough set is an expansion of the classical rough set by using multiple
granular structures. Presently, three important multigranulation rough sets have been …

As an extension of Pawlak rough set model, decision-theoretic rough set model (DTRS)
adopts the Bayesian decision theory to compute the required thresholds in probabilistic …