Granular computing, as a new and rapidly growing paradigm of information processing, has
attracted many researchers and practitioners. Granular computing is an umbrella term to …

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 …

Classical rough set theory is considered a useful tool for dealing with the uncertainty of
categorical data. The major deficiency of this model is that the classical rough set model is …

We propose a framework for the study of covering based rough set approximations. Three
equivalent formulations of the classical rough sets are examined by using equivalence …

In this paper, we contribute to a recent and successful modelization of uncertainty, which the
practitioner often encounters in the formulation of multicriteria group decision making …

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 …

Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …

Fuzzy, rough, and soft sets are different mathematical tools mainly developed to deal with
uncertainty. Combining these theories has a wide range of applications in decision analysis …

At present, there is no unified method for solving multiattribute decision-making problems. In
this paper, we propose two methods that benefit from some novel fuzzy rough set models …

By introducing the new concepts of fuzzy β-covering and fuzzy β-neighborhood, we define
two new types of fuzzy covering rough set models which can be regarded as bridges linking …