摘要
模糊粗糙集融合了模糊集和粗糙集的思想,是一种新的处理模糊和不确定性知识的软计算工具。针对属性为模糊值的信息系统,提出了一种基于熵的模糊粗糙集知识获取方法:首先通过模糊相似度量计算出各属性下对象的模糊相似值,再根据模糊相似关系构造模糊等价关系,然后根据模糊等价关系建立属性集的信息熵表示,继而使用基于信息熵的决策表属性约简算法获取规则。最后,通过一个实例,分析说明了这种算法的合理有效性。
Fuzzy rough set,which combines the ideas of fuzzy set and rough set,is a new soft computing tool dealing with fuzzy and uncertain information.In this paper the authors propose an entropy-based knowledge acquisition approach to handle information system whose attribute values are fuzzy.Firstly,the authors calculate fuzzy indiscernibility values between objects in each attribute based on fuzzy similarity measure.With these values the authors can construct fuzzy equivalence relations among objects. Then the authors can calculate the information entropy of any attribute set.The entropy-based attribute reduction algorithm can he used to acquire rules.Finally,by an example,the approach is verified to be reasonable and effective.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第23期59-61,126,共4页
Computer Engineering and Applications
关键词
模糊粗糙集
模糊相似度量
模糊等价关系
信息熵
fuzzy rough set
fuzzy similarity measure
fuzzy equivalence relations
information entropy
