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 is one of the most important applications of fuzzy rough sets in machine
learning and pattern recognition. Most existing methods employ the intersection operation of …

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 …

This paper concerns the intuitionistic fuzzy (IF) decision-making method for solving uncertain
or vague problems. This traditional method has imperfections in some particular …

Intuitionistic fuzzy sets and rough sets are two different mathematical models to deal the
problem of how to understand and manipulate imperfect knowledge. An intuitionistic fuzzy …

Three-way decisions have become a representative of trisecting-and-acting model for
uncertainty data analysis. With granular computing point of view, a more reasonable …

This article studies the rough approximation of a vague concept based on multiple
granularity over two universes. With the description of the background of risk decision …

As an extension of Pythagorean fuzzy sets, the q‐rung orthopair fuzzy sets (q‐ROFSs) can
easily solve uncertain information in a broader perspective. Considering the fine property of …

In this paper axiomatic characterizations of relation-based intuitionistic fuzzy rough
approximation operators determined by an intuitionistic fuzzy triangular norm T and its dual …

This paper develops a single-granulation hesitant fuzzy rough set (SGHFRS) model from the
perspective of granular computing. In the multi-granulation framework, we propose two types …