A novel uncertainty measure on rough sets: A mean-variance approach

Uncertainty measure is an important implement for characterizing the degree of uncertainty in rough set theory. It has been extensively applied in pattern recognition and data clustering. However, this paper reveals the issue that classical uncertainty measures are sensitive to disturbances or noises. Therefore, a novel uncertainty measure, called mean-variance measure (MVM), is proposed to characterize the degree of uncertainty of rough sets. Since it takes fully information in the boundary region into account, MVM is more robust and effective than classical uncertainty measures in depressing disturbances and noises.