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

Probabilistic approaches have been applied to the theory of rough set in several forms,
including decision-theoretic analysis, variable precision analysis, and information-theoretic …

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 …

Decision-theoretic rough set model can derive several probabilistic rough set models by
providing proper cost functions. Learning cost functions from data automatically is the key to …

Multigranulation rough set theory is a desirable direction in the field of rough set, in which
upper and lower approximations are approximated by multiple granular structures. However …

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 …

The original rough set model, ie, Pawlak's single-granulation rough set model has been
extended to a multigranulation rough set model, where two kinds of multigranulation …

Multigranulation rough sets (MGRSs) where a target concept is approximated by granular
structures induced by multiple binary relations have been applied successfully in many …

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 of discourse. The 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 …