Many attribute reduction methods have been proposed for decision-theoretic rough set model based on different definitions of attribute reduct, while an attribute reduct can be seen as an attribute subset that satisfies specific criteria. Most reducts are defined on the basis of a single criterion, which may result in the difficulty for users to choose appropriate reduct to design related reduction algorithm. To address this problem, we propose a multi-objective attribute reduction method based on NSGA-II for decision-theoretic rough set model. Three different definitions of attribute reduct based on positive region, decision cost and mutual information are considered and transferred to a multi-objective optimization problem. Experimental results show that the multi-objective reduction method can obtain a robust and better classification performance.
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