Approaches to knowledge reduction based on interval and set-valued decision information systems

Rough set theory, proposed by Pawlak [1], is an extension of set theory for the study of intelligent systems characterized by insucient and incomplete information. It has conceived as a tool to conceptualize and analyze various types of data. With more than twenty years development, rough set theory has important applications to intelligence decision, cognitive sciences, machine learning, pattern recognition, and so on.