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Region Vector Based Attribute Reducts in Decision-Theoretic Rough Sets

机译:决策理论粗糙集中基于区域矢量的属性约简

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When removing some attributes, the partition induced by a smaller set of attributes will be coarser and the decision regions may be changed. In this paper, we analyze the decision region changes when removing attributes and propose a new type of attribute reducts from the point of view of vector based three-way approximations of a partition. We also present a reduct construction method by using a discernibility matrix.
机译:当删除某些属性时,由较小的一组属性引起的分区将更粗糙,并且决策区域可能会更改。在本文中,我们分析了移除属性时决策区域的变化,并从基于矢量的分区三向逼近的角度提出了一种新型的属性约简。我们还提出了一种使用区分矩阵的还原构造方法。

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