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A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets

机译:带有模糊粗糙集的归约法的一种新算法

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摘要

Attribute reduction is one of the most meaningful research topics in the existing fuzzy rough sets, and the approach of discernibility matrix is the mathematical foundation of computing reducts. When computing reducts with discernibility matrix, we find that only the minimal elements in a discernibility matrix are sufficient and necessary. This fact motivates our idea in this paper to develop a novel algorithm to find reducts that are based on the minimal elements in the discernibility matrix. Relative discernibility relations of conditional attributes are defined and minimal elements in the fuzzy discernibility matrix are characterized by the relative discernibility relations. Then, the algorithms to compute minimal elements and reducts are developed in the framework of fuzzy rough sets. Experimental comparison shows that the proposed algorithms are effective.
机译:属性约简是现有模糊粗糙集中最有意义的研究主题之一,可分辨矩阵的方法是计算约简的数学基础。在使用可区分性矩阵计算约简时,我们发现仅可区分性矩阵中的最小元素是足够和必要的。这一事实激发了我们在本文中的想法,以开发一种新颖的算法来找到基于可分辨矩阵中的最小元素的约简。定义了条件属性的相对可分辨关系,并通过相对可分辨关系来表征模糊可分辨矩阵中的最小元素。然后,在模糊粗糙集的框架内发展了计算最小元素和约简的算法。实验比较表明,该算法是有效的。

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  • 来源
    《Fuzzy Systems, IEEE Transactions on》 |2012年第2期|p.385-389|共5页
  • 作者

    Degang Chen;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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