Three-way approximate reduct based on information-theoretic measure

Three-way decision is a typical and popular methodology for decision-making and approximate reasoning, while attribute reduction is an important research topic in three-way decision. However, most attribute reduction methods based on three-way decision strictly rely on the preservation of measure criterion, which not only explicitly limits the efficiency of attribute reduction and also implicitly confines the generalization ability of the resulting reduct. In this study, we present a new three-way approximate attribute reduction method based on information-theoretic measure. More specifically, a unified framework for approximate attribute reduction is first provided. Then, the process of attribute reduction is considered to determine each attribute to be the positive region, boundary region, or negative region in terms of its correlation to the decision attribute. The negative attributes can be removed by the preservation of information-theoretic measure, while some boundary attributes are further iteratively eliminated by relaxing the measure criterion. An approximate reduct is finally formed by the positive attributes and the remaining boundary attributes. On several public UCI data sets, the proposed method achieves a much better attribute reduction rate and simultaneously gains an improvement in performance when comparing with other attribute reduction methods.