RFRR: Robust Fuzzy Rough Reduction
RFRR: Robust Fuzzy Rough Reduction
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RFRR：鲁棒模糊粗糙约简
DOI：
10.1109/tfuzz.2012.2231417
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发表时间：
2013-10
影响因子：
11.9
通讯作者：
Suyun Zhao, Hong Chen, Cuiping Li, Mengyao Zhai,
中科院分区：
文献类型：
--
作者：
Suyun Zhao, Hong Chen, Cuiping Li, Mengyao Zhai,
本文提出了一种鲁棒的模糊粗糙集降维方法，该方法的降维结果能够反映所有可能参数下的降维结果。这里，在所有可能参数上获得的约简意味着所有约简是在处理噪声的不同鲁棒性程度上获得的。该方法与现有的模糊粗糙约简方法完全不同。差异主要表现在概念、工具和算法三个方面。首先，重新定义了属性约简的核心概念。也就是说，鲁棒模糊粗糙约简，这是缩短到一个鲁棒约简，提出反映所有可能的参数上获得的经典约简。新的“鲁棒约简”不是条件属性的清晰子集;相反，它是一个模糊子集，其最有趣的性质是鲁棒约简的任何截集都是某个参数上的经典约简。第二，用于测量可接受性能力的工具不同于现有的可接受性措施。在本文中，每个属性的鲁棒性处理误分类和扰动被认为是。综合考虑鲁棒性和可辨识性，设计了鲁棒模糊可辨识性矩阵。最后，基于鲁棒模糊可达性矩阵设计了鲁棒约简算法，这与现有的经典约简算法完全不同。
This paper proposes a robust method of dimension reduction using fuzzy rough sets, in which the reduction results can reflect the reducts obtained on all of the possible parameters. Here, the reducts being obtained on all of the possible parameters mean that all of the reducts are obtained on different degrees of robustness to handle noise. This method is completely different from the existing methods of fuzzy rough reduction. The differences are shown in three aspects: the concept, the tool, and the algorithm. First, the key concept of attribute reduction is redefined in a new way. That is, the robust fuzzy rough reduct, which is shortened to a robust reduct, is proposed to reflect the classical reducts obtained on all of the possible parameters. The new “robust reduct” is not a crisp subset of condition attributes; rather, it is a fuzzy subset, whose most interesting property is that any cut set of the robust reduct is a classical reduct on a certain parameter. Second, the tool used to measure the discernibility power is different from the existing discernibility measures. In this paper, the robustness of each attribute to handle misclassification and perturbation is considered. By considering both the robustness and the discernibility, a robust fuzzy discernibility matrix is designed. Finally, the algorithms used to find the robust reducts are designed based upon the robust fuzzy discernibility matrix, which is completely different from the existing algorithms used to find the classical reducts.
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DOI：
10.1201/b10345-5
发表时间：
2010-11
期刊：
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影响因子：
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作者：
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DOI：
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期刊：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1007/s13042-010-0003-y
发表时间：
2010-09
影响因子：
5.6
作者：
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通讯作者：
Kevin Small;D. Roth
影响因子：
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作者：
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