代价敏感的多粒度邻域粗糙模糊集的近似表示

Cost-sensitive Multigranulation Approximation of Neighborhood Rough Fuzzy Sets

多粒度邻域粗糙集是邻域粗糙集理论的一种新型数据处理模式,其目标概念分别由乐观和悲观的上、下近似边界描述。但当前的多粒度邻域粗糙集既缺乏利用已有的信息粒近似描述目标概念的方法,又无法处理目标概念为模糊的情形。而张清华教授提出的粗糙集近似理论提供了一种利用已有信息粒近似描述知识的方法,为构建多粒度邻域粗糙模糊集的近似精确集提供了新思路。文中首先针对模糊目标概念,将粗糙集近似理论应用到邻域粗糙集领域,提出了代价敏感的邻域粗糙模糊集的近似表示模型;然后进一步从多粒度视角,构建出一种代价敏感的邻域粗糙模糊集的多粒度近似表示模型,并分析了其相关性质;最后,通过实验仿真,验证了当多粒度代价敏感近似及其上、下近似方法分别去近似刻画模糊目标概念时,多粒度代价敏感近似方法产生的误分类代价最小。

Multigranulation neighborhood rough sets are a new data processing mode in the theory of neighborhood rough sets,in which the target concept can be characterized by upper/lower approximate boundaries of optimistic and pessimistic,respectively.Nevertheless,the current multigranulation neighborhood rough sets not only lacks the method of using the existing information granules to describe the target concept approximately,but also can not deal with the situation that the target concept is fuzzy Whereas the approximation theory of rough sets proposed by professor Zhang provides a method for approximately describing knowledge utilizing existing information granules,therefore,it provides a new method for constructing approximate and accurate sets of multigranulation neighborhood rough fuzzy sets.In this paper,aiming to process the fuzzy target concept,the approximation theory of rough sets is applied to the field of neighborhood rough sets,and a cost-sensitive approximate representation model of neighborhood rough fuzzy sets is introduced.Then,from the perspective of multigranulation,a multigranulation approximate representation model of the cost-sensitive neighborhood rough fuzzy sets is constructed with evaluating its related properties.Finally,simulation results show that when the multigranulation cost-sensitive approximation and upper/lower approximation are used to approximate the fuzzy target concept,the multigranulation cost-sensitive approximation method reaches the least misclassification cost.