对于覆盖近似空间中粗糙集的不确定性度量,目前的方法主要有粗糙度、粗糙熵和模糊度.通过分析这些不确定性度量方法,发现在特定的情况下它们都存在一定的不合理性.提出一种粗糙集的模糊度,给出并证明了相关性质.分析表明该度量方法克服了已有方法存在的不合理性,为覆盖粗糙集的不确定性度量提供了方法.%For uncertainty measuring of rough sets in covering approximation space, the major approaches in use are rough degree, rough entropy and fuzzy degree. Through analyzing these uncertainty measuring methods, it is revealed that they all bear some irrationality under specified situations. A fuzzy degree of rough sets is proposed while its relative properties are provided and validated. Analysis has proven that the uncertainty measuring approach overcomes the irrationalities of former approaches, so it provides a method for measuring the uncertainty of covering rough sets.
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