摘要
考虑到粗糙集的不确定性与其所在近似空间知识粒度的关系,在属性粗糙集模型的基础上,将传统的粗糙度与知识粒度相结合,提出了一种新的属性粗糙集粗糙性的度量方法,讨论了这一度量的特性.证明了随着近似空间的细分新的粗糙度单调减小的性质.
Based on the fact that the uncertainty of rough sets is closely related to the knowledge granularities of the same approximation space,a new method for measuring the roughness of attribute rough sets is proposed by integrating the traditional roughness with knowledge granularities. We discuss the properties of this measure and prove that this new roughness is decreasing with the refinement of knowledge granularities in approximation space.
出处
《烟台大学学报（自然科学与工程版）》
CAS
2015年第4期235-238,共4页
Journal of Yantai University(Natural Science and Engineering Edition)
基金
国家自然科学基金资助项目(61179038)
河南省自然科学基金资助项目(132300410391)
关键词
属性粗糙集
属性测度
知识粒度
粗糙度
attribute rough set
attribute measure
knowledge granularity
roughness measure
