Abstract
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.
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