Multigranulation decision-theoretic rough sets

The Bayesian decision-theoretic rough sets propose a framework for studying rough set approximations using probabilistic theory, which can interprete the parameters from existing forms of probabilistic approaches to rough sets. Exploring rough sets in the viewpoint of multigranulation is becoming one of desirable directions in rough set theory, in which lower/upper approximations are approximated by granular structures induced by multiple binary relations. Through combining these two ideas, the objective of this study is to develop a new multigranulation rough set model, called a multigranulation decision-theoretic rough set. Many existing multigranulation rough set models can be derived from the multigranulation decision-theoretic rough set framework.

► Multigranulation decision-theoretic rough sets are proposed. ► When parameters satisfy some constraints, it will derive many kinds of multigranulation rough sets. ► The relationships among multigranulation decision-theoretic rough sets, decision-theoretic rough sets and single granulation rough sets are established.