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 …

ABSTRACT The Rough Set (RS) theory can be considered as a tool to reduce the input
dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there …

After probability theory, fuzzy set theory and evidence theory, rough set theory is a new
mathematical tool for dealing with vague, imprecise, inconsistent and uncertain knowledge …

Worldwide, there has been a rapid growth in interest in rough set theory and its applications
in recent years. Evidence of this can be found in the increasing number of high-quality …

This paper presents a framework for a systematic study of the rough set theory. Various
views and interpretations of the theory and different approaches to study the theory are …

Fuzzy rough approximation operators are the underlying concepts in fuzzy rough set theory.
There are at least two approaches to develop these primary concepts, ie, the constructive …

Rough set theory is a computational model of approximate reasoning proposed by Z.
Pawlak in 1982, which captures a given concept as two approximations represented by …

The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy
set, sometimes with a view to prove that one is more general, or, more useful than the other …

This paper summarizes various formulations of the standard rough set theory. It
demonstrates how those formulations can be adopted to develop different generalized …

Rough set theory can be perceived as a new approach to vagueness and uncertainty. It
seems to be of fundamental importance to AI and cognitive sciences, especially in the areas …