Feature selection acts as an important preprocessing method to reduce redundant
information. In order to effectively evaluate the classification information hidden in a given …

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 …

Recently, a multigranulation rough set (MGRS) has become a new direction in rough set
theory, which is based on multiple binary relations on the universe. However, it is worth …

Probabilistic approaches to rough sets in granulation, approximation and rule induction are
reviewed. The Shannon entropy function is used to quantitatively characterize partitions of a …

Granular computing emerges as a new multi-disciplinary study and has received much
attention in recent years. A conceptual framework is presented by extracting shared …

There are two objectives of this chapter. One objective is to examine the basic principles and
issues of granular computing. We focus on the tasks of granulation and computing with …

Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to
describe the uncertain information approximately in rough set theory. Certain and uncertain …

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 …

Rough set theory has proven to be an efficient tool for modeling and reasoning with
uncertainty information. By introducing probability into fuzzy approximation space, a theory …