Knowledge reduction in decision-theoretic rough set model based on connection degree

Knowledge reduction is one of the most important research issues in decision-theoretic rough set model. This paper first defines a new attribute measure for a reduct preserving boundary region partition, then constructs a connection degree to evaluate the different candidate reducts, and finally proposes a knowledge reduction algorithm for decision-theoretic rough set model. Example analysis shows that this algorithm is valid.