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 …

Rough set theory is an efficient tool for dealing with inexact and uncertain information.
Numerous studies have focused on rough set theory and associated methodologies, and in …

Attribute reduction serves as a pivotal topic of rough set theory for data analysis. The ideas
of tri-level thinking from three-way decision can shed new light on three-level attribute …

It is not widely acknowledged that none of existing fuzzy β-neighborhood operators satisfies
the reflexivity when β≠ 1. To overcome this shortcoming, four types of fuzzy β-neighborhood …

In real-applications, there may exist many kinds of data (eg, boolean, categorical, real-
valued and set-valued data) and missing data in an information system which is called as a …

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 …

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

Feature selection via mutual information has been widely used in data analysing. Mutual
information with monotonous is an effective tool to analyse the correlation and redundancy …

The concept of attribute reducts plays a fundamental role in rough set analysis. There are at
least two possibilities to define an attribute reduct. A classification-based or global attribute …

Attribute reduction is one of the most meaningful research topics in the existing fuzzy rough
sets, and the approach of discernibility matrix is the mathematical foundation of computing …