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An Improved Attribute Reduction Algorithm Based on Importance of Attribute Value

机译:基于属性值重要性的改进属性缩减算法

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The theory of rough set is a new mathematical tool to deal with the uncertain problems, and the attribute reduction is one of key problems in the theory. To obtain a better method of attributes reduction in decision-making system, a concept of restrictive positive region was proposed. The method using positive region and restrictive positive region which have been obtained, shrinks the scope of data processing, so that reduces the time demand. By an instance, the paper presents the application of this method, and confirms that the computation is reduced and the result could be simpler, using this algorithm compared with the traditional algorithm. Thus, it is proven that this method is a fast and efficient algorithm of attribute reduction.
机译:粗糙集理论是一种解决不确定问题的新数学工具,并且属性减少是理论中的关键问题之一。为了获得决策系统的更好的属性方法,提出了一个限制性正区域的概念。使用正区域和限制正区域的方法已经获得,缩小了数据处理的范围,从而降低了时间需求。通过一个实例,本文提出了这种方法的应用,并确认计算减少了,并且与传统算法相比,使用该算法可以更简单。因此,证明该方法是一种快速高效的属性算法。

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