As a natural extension of three-way decisions with incomplete information, this paper
provides a novel three-way decision model based on incomplete information system. First …

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy
rough dependency as a criterion for feature selection. However, this model can merely …

Recently, by combining rough set theory with granular computing, pessimistic and optimistic
multigranulation rough sets have been proposed to derive “AND” and “OR” decision rules …

There exist several approaches to rough set approximations in a multigranulation space,
namely, a family of equivalence relations. In this paper, we propose a unified framework to …

Four kinds of constructive methods of rough approximation operators from existing rough
sets are established, and the important conclusion is obtained: some rough sets are …

Attribute reduction is a key issue in rough set theory which is widely used to handle
uncertain knowledge. However, most existing attribute reduction approaches focus on cost …

Granule description is a fundamental problem in granular computing. Although the spirit of
granular computing has been widely adopted in scientific researches, how to classify and …

Interval-valued data appear as a way to represent the uncertainty affecting the observed
values. Dealing with interval-valued information systems is helpful to generalize the …

Abstract Knowledge reduction is one of the key issues in knowledge discovery and data
mining. During the construction of a concept lattice, it has been recognized that …

Uncertainty measure mining and applications are fundamental, and it is possible for double-
quantitative fusion to acquire benign measures via heterogeneity and complementarity. This …