Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …

Most extant feature selection works neglect interactive features in the form of groups, leading
to the omission of some important discriminative information. Moreover, the prevalence of …

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and
artificial intelligence, especially in numerous fields such as expert systems, knowledge …

Three-way decision model (3WDM) with decision-theoretical rough sets (DTRSs) always
addresses precise cost parameters and precise attribute values in uncertain problem …

Uncertainty measures can supply new viewpoints for analyzing data. They can help us in
disclosing the substantive characteristics of data. The uncertainty measurement issue is also …

Dominance-based rough set approach has attracted much attention in practical applications
ever since its inception. This theory has greatly promoted the research of multi-criteria …

Among the large amount of genes presented in microarray gene expression data, only a
small fraction of them is effective for performing a certain diagnostic test. In this regard, a …

Attribute reduction is viewed as an important preprocessing step for pattern recognition and
data mining. Most of researches are focused on attribute reduction by using rough sets …

Attribute reduction with fuzzy rough sets is to obtain a compact and informative attribute
subset from the original attribute set, in which the construction of the attribute evaluation …

The main challenge of attribute reduction in large data applications is to develop a new
algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data …