  Rough set theory is a well-known mathematical framework that can deal with
inconsistent data by providing lower and upper approximations of concepts. A
prominent property of these approximations is their granular representation:
that is, they can be written as unions of simple sets, called granules. The
latter can be identified with "if. . . , then. . . " rules, which form the
backbone of rough set rule induction. It has been shown previously that this
property can be maintained for various fuzzy rough set models, including those
based on ordered weighted average (OWA) operators. In this paper, we will focus
on some instances of the general class of fuzzy quantifier-based fuzzy rough
sets (FQFRS). In these models, the lower and upper approximations are evaluated
using binary and unary fuzzy quantifiers, respectively. One of the main targets
of this study is to examine the granular representation of different models of
FQFRS. The main findings reveal that Choquet-based fuzzy rough sets can be
represented granularly under the same conditions as OWA-based fuzzy rough sets,
whereas Sugeno-based FRS can always be represented granularly. This observation
highlights the potential of these models for resolving data inconsistencies and
managing noise.
