The original rough set model was developed by Pawlak, which is mainly concerned with the
approximation of sets described by a single binary relation on the universe. In the view of …

The Bayesian decision-theoretic rough sets propose a framework for studying rough set
approximations using probabilistic theory, which can interprete the parameters from existing …

The original rough-set model is primarily concerned with the approximations of sets
described by a single equivalence relation on a given universe. With granular computing …

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 …

Multigranulation rough sets (MGRS) is one of desirable directions in rough set theory, in
which lower/upper approximations are approximated by granular structures induced by …

One of the strengths of rough set theory is the fact that an unknown target concept can be
approximately characterized by existing knowledge structures in a knowledge base …

Set-valued ordered information systems can be classified into two categories: disjunctive
and conjunctive systems. Through introducing two new dominance relations to set-valued …

Engineering design processes comprise highly creative and knowledge-intensive tasks that
involve extensive information exchange and communication among distributed teams. In …

Many real data sets in databases may vary dynamically. With the rapid development of data
processing tools, databases increase quickly not only in rows (objects) but also in columns …

Local rough sets as a generalization of classical rough sets not only inherit the advantages
of classical rough sets which can handle imprecise, fuzzy and uncertain data, but also break …