Firstly, the concepts of discernibility degree and relative discernibility degree are presented based on general binary
relations. Then the properties of these concepts are analyzed. Furthermore, an efficient attribute reduction algorithm is
designed based on the relative discernibility degree. Especially, the attribute reduction algorithm is able to deal with
various kinds of extended models of classical rough set theory, such as the tolerance relation-based rough set model,
non-symmetric similarity relation-based rough set model. Finally, the theoretical analysis is backed up with numerical
examples to prove that the proposed reduction method is an effective technique to select useful features and eliminate
redundant and irrelevant information.