Fault-tolerant enhanced bijective soft set with applications

Fault-tolerant enhanced bijective soft set with applications
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容错增强型双射软集及其应用

DOI:
10.1016/j.asoc.2016.06.009
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发表时间:
2017-05
影响因子:
8.7
通讯作者:
Yi Peng
Yi Peng
中科院分区:
计算机科学2区
文献类型:
--
作者:
Ke Gong;Panpan Wang;Yi Peng

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双射软集作为软集的一种扩展,可以用于软集环境下的数据挖掘,并在一些领域得到了研究和应用。然而,只有一小部分的故障数据将导致双射软集失去主要的识别能力的挖掘数据。因此,本研究旨在提高基于双射软集的数据挖掘方法对数据的容错能力。首先定义了β-错分类度下的双射软集的概念和运算。此外,算法找到一个最佳的β,减少,核心,决策规则和错误分类的数据。最后,通过一个真实的岸线资源评价规则获取问题对模型进行了验证。结果表明,该模型具有容错能力,提高了基于双射软集的数据挖掘方法的容错能力。此外,所提出的方法可以帮助决策者发现故障数据进行进一步的分析。
As an extension of the soft set, the bijective soft set can be used to mine data from soft set environments, and has been studied and applied in some fields. However, only a small proportion of fault data will cause bijective soft sets losing major recognition ability for mining data. Therefore, this study aims to improve the bijective soft set-based data mining method on tolerate-fault-data ability. First some notions and operations of the bijective soft set at a β-misclassification degree is defined. Moreover, algorithms for finding an optimal β, reductions, cores, decision rules and misclassified data are proposed. This paper uses a real problem in gaining shoreline resources evaluation rules to validate the model. The results show that the proposed model has the fault-tolerant ability, and it improves the tolerate-ability of bijective soft set-based data mining method. Moreover, the proposed method can help decision makers to discover fault data for further analysis.
DOI: 10.1016/j.mcm.2010.11.080
发表时间: 2011-03
期刊: Math. Comput. Model.
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