覆盖生成粗糙集的不确定性测度

2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops Pub Date : 2006-12-18 DOI:10.1109/WI-IATW.2006.139

Jun Hu, Guoyin Wang, Qinghua Zhang

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引用次数: 17

摘要

不确定性度量是基于覆盖近似空间的知识发现的关键问题。将信息熵分别应用于经典粗糙集和扩展粗糙集来度量不确定性，但尚未研究它们之间的关系。基于等域关系，将覆盖近似空间转化为分区近似空间，给出了覆盖生成粗糙集的不确定性测度。用所生成的分区近似空间定义分析了它们的性质，并在原有的覆盖近似空间定义下保持了这些性质

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Uncertainty Measure of Covering Generated Rough Set

Uncertainty measure is a key issue of knowledge discovery based on covering approximation space. Information entropy was applied into both classical rough set and extended rough set to measure the uncertainty respectively, but their relationship has not been studied. Based on equal domain relation, a covering approximation space is converted into a partition approximation space in this paper, and uncertainty measures of covering generated rough set are developed. Their properties are analyzed with the generated partition approximation space fined, and these properties are still kept with the original covering approximation spacer fined

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来源期刊

2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops

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