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Attribute reduction of covering decision systems by hypergraph model

机译：超图模型的覆盖决策系统属性约简

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Attribute reduction (also called feature subset selection) plays an important role in rough set theory. Different from the classical attribute reduction algorithms, the methods of attribute reduction based on covering rough sets appear to be suitable for numerical data. However, it is time-consuming in dealing with the large-scale data. In this paper, we study the problem of attribute reduction of covering decision systems based on graph theory. First, we translate this problem into a graph model and show that finding the attribute reduction of a covering decision system is equivalent to finding the minimal vertex cover of a derivative hypergraph. Then, based on the proposed model, a thm for covering decision systems is presented. Experiments show that the new proposed method is more effective to handle the large-scale data. (C) 2016 Elsevier B.V. All rights reserved.
机译：属性约简（也称为特征子集选择）在粗糙集理论中起着重要作用。与经典的属性约简算法不同，基于覆盖粗糙集的属性约简方法似乎适合于数值数据。但是，在处理大规模数据时非常耗时。本文研究基于图论的覆盖决策系统的属性约简问题。首先，我们将此问题转换为图形模型，并表明发现覆盖决策系统的属性约简等效于找到导数超图的最小顶点覆盖。然后，基于提出的模型，提出了一种用于覆盖决策系统的模型。实验表明，该新方法对处理大规模数据更有效。 （C）2016 Elsevier B.V.保留所有权利。

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