%0 Journal Article
%T 基于证据理论刻画多粒度覆盖粗糙集的数值属性
%A 车晓雅
%A 李磊军
%A 米据生
%J 智能系统学报
%D 2016
%R 10.11992/tis.201606011
%X 在经典多粒度粗糙集模型的基础上，基于论域中对象的极大描述和极小描述，定义了4种应用更为广泛的悲观多粒度覆盖粗糙集模型。然后通过集合的交、并运算与关系划分函数，构造了对象关于覆盖族的单粒度的多元覆盖及单粒度划分。在此基础上，基于证据理论，探讨了4种悲观多粒度覆盖粗糙集的上、下近似与信任函数和似然函数之间关系，并描述了该模型所具备的相关数值属性。对比分析表明悲观多粒度覆盖粗糙集模型既具备经典多粒度粗糙集模型能够融合多源信息的优势，又克服了其应用范围狭窄的缺点。实例分析验证了所提模型的有效性。</br>Considering classical multi-granulation rough sets and using the maximal and minimal descriptors of objects in a given universe, this paper proposes four pessimistic multi-granulation covering rough set models, suitable for extensive application. Based on set union and portion functions, the notion of multi-granularity covering connected to a number of coverings and a single granularity partition in the domain are defined. On this basis, belief and plausibility functions from evidence theory are employed to define the relationship between the upper and lower approximations, the belief function, and the likelihood function, and to characterize the set approximations in the four models. Compared with classical multi-granulation rough sets, the pessimistic multi-granulation covering rough set models not only have distinct advantages and combine multi-source information, but also avoid the shortcomings of a narrow application range. Finally, a real example is used to demonstrate the effectiveness of the presented models
%K 粗糙集理论
%K 覆盖
%K 粒度
%K 证据理论
%K 近似
%K 特性描述&lt
%K /br&gt
%K rough sets theory
%K covering
%K granulation
%K evidence theory
%K approximation
%K characterization
%U http://tis.hrbeu.edu.cn/oa/darticle.aspx?type=view&id=20160407