Multicost Decision-theoretic rough sets based on maximal consistent blocks

Abstract

Decision-theoretic rough set comes from Bayesian decision procedure, in which a pair of the thresholds is derived by the cost matrix for the construction of probabilistic rough set. However, classical decision-theoretic rough set can only be used to deal with complete information systems. Moreover, it does not take the property of variation of cost into consideration. To solve above two problems, the maximal consistent block is introduced into the construction of decision-theoretic rough set by using multiple cost matrixes. Our approach includes optimistic and pessimistic multicost decision-theoretic rough set models. Furthermore, the whole decision costs of optimistic and pessimistic multicost decision-theoretic rough sets are calculated in decision systems. This study suggests potential application areas and new research trends concerning decision-theoretic rough set.