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The further investigation of variable precision intuitionistic fuzzy rough set model

机译:变精度直觉模糊粗糙集模型的进一步研究

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摘要

By applying weighted aggregation operator, we firstly define the similarity measure between two intuitionistic fuzzy sets, and we prove that it is also a -equivalence intuitionistic fuzzy relation which is called weighted -equivalence intuitionistic fuzzy relation. However, different attributes have different significance, to measure the importance of each attribute, in this article, we use variable precision intuitionistic fuzzy rough set(VPIFRS) to process the data in decision table and obtain the weight of each condition attribute. Thus, a new -equivalence intuitionistic fuzzy partition is obtained based on the weighted -equivalence intuitionistic fuzzy relation and the weight set of condition attribute, it shows that this partition is more suitable and less sensitive to perturbation. Subsequently, to determine a rational change interval for threshold we investigate the -stable intervals. Simultaneously, we discuss the two types uncertainty of VPIFRS theory, and show that it can be characterized by information entropy and the rough degree. Finally, an example is given to illustrate our results, which show that our method is more feasible and less sensitive to perturbation and misclassification.
机译:通过应用加权聚合算子,首先定义了两个直觉模糊集之间的相似性度量,证明了这也是等价直觉模糊关系,称为加权等价直觉模糊关系。然而,不同的属性具有不同的意义,以衡量每个属性的重要性,本文中,我们使用可变精度直觉模糊粗糙集(VPIFRS)处理决策表中的数据并获得每个条件属性的权重。因此,基于加权等价直觉模糊关系和条件属性的权重集,得到了一个新的等价直觉模糊分区,表明该分区更合适,对扰动的敏感性较小。随后,为了确定阈值的合理变化间隔,我们研究了-稳定间隔。同时,我们讨论了VPIFRS理论的两种不确定性,并表明它可以用信息熵和粗糙程度来表征。最后,给出一个例子来说明我们的结果,表明我们的方法更可行,并且对扰动和错误分类不太敏感。

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