基于固定β值的概念格上变精度粗糙集近似

粗糙集理论和概念格理论均为研究知识发现与不确定性决策问题的重要方法,二者之间紧密相关。在提出概念格上的变精度粗糙集的β-上、下近似定义的基础上,一方面,对于任意给定的变精度β,讨论了概念格上变精度粗糙集β-上、下近似的性质;另一方面,针对不可定义对象集,分别提出了概念格上的变精度粗糙集β-上、下近似算法;最后,实例验证了新给出的算法可以满足用户对不同近似精度的要求,使近似结果有弹性的变化,较Yao和Monhanty给出的算法有一定的优势。

Variable precision rough set approximations in concept lattice based on the fixed value of β

Both rough set theory and concept lattice theory are important methods for knowledge discovery and uncertainty decision, and they are closely connected. Based on the concepts of β-upper and lower approximations of variable precision rough sets in a concept lattice, we discuss the properties of the β-upper and β-lower approximations of variable precision rough sets in a concept lattice for any given variable precision β, and for any undefinable object in a formal context, we propose an algorithm about β-upper and β-ower approximations of variable precision rough sets in a concept lattice, respectively. What’s more, an example is given to verify that the new algorithm can meet the requirements of users’ different approximations and make the approximation results change flexibly. The new algorithm outperforms Yao and Mohanty' algorithms in β-lower approximations.