Rough set theory is an important approach for data mining, and it refers to Shannon's
information measures for uncertainty measurements. The existing local conditional …

In terms of rough set theory, information-theoretical measures have been introduced to
implement uncertainty measurements and system applications, and their robust construction …

Rough set theory is an important theory for the uncertain information processing. The
information theoretic measures have been introduced into rough set theory and provided a …

Attribute reduction in rough set theory serves as a fundamental topic for information
processing, and its basis is usually a decision table (D-Table). D-Table attribute reduction …

As two important expanded quantification rough sets models, the multigranulation decision-
theoretic rough sets mainly uses conditional probability to show relative quantitative …

Rough set theory (RS-Theory) is a fundamental model of granular computing (GrC) for
uncertainty information processing, and information entropy theory provides an effective …

Multi-granularity rough sets facilitate knowledge-based granular computing, and their
compromised models (called CMGRSs) outperform classical optimistic and pessimistic …

Uncertainty measures can supply new points of view for analyzing data and help us to
disclose the substantive characteristics of data sets. Accuracy and roughness proposed by …

Probabilities and grades serve as relative and absolute measures, respectively. They are
used to establish the decision-theoretic rough set (DTRS) and graded rough set (GRS)–two …

The multi-granulation rough sets serve as important hierarchical models for intelligent
systems. However, their mainstream optimistic and pessimistic models are respectively too …