A new uncertainty measure of rough sets
A new uncertainty measure of rough sets
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DOI：
10.1109/robio.2009.5420845
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Shuhua Teng;Dingqun Zhang;Lingyun Cui;Jixiang Sun;Zhiyong Li
中科院分区：
文献类型：
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作者：
Shuhua Teng;Dingqun Zhang;Lingyun Cui;Jixiang Sun;Zhiyong Li
不确定性度量是知识发现和数据挖掘的关键问题。粗糙集理论(RST)是度量和处理不确定信息的重要工具。尽管已经研究了许多基于RST的系统不确定性度量方法，但现有的度量方法不能很好地刻画粗糙集的不精确性。为了克服这些缺点，我们提出了一种基于属性区分能力的不确定性度量。理论分析和数值算例表明，新方法不仅克服了现有方法的局限性，而且符合人类的认知。
Uncertainty measure is a key issue for knowledge discovery and data mining. Rough set theory (RST) is an important tool for measuring and handling uncertain information. Although many RST-based methods to measure system uncertainty have been investigated, the existing measures are not able to characterize well the imprecision of a rough set. To overcome the shortcomings, we present a well-justified measure of uncertainty based on discernibility capability of attributes. The theoretical analysis is backed up with numerical examples to prove that our new method does not only overcome the limitations of the existing measures but also consist with human cognition.