决策粗糙集理论研究现状与展望

摘要

经典 Pawlak 粗糙集理论中的核心概念上、下近似集是通过集合相交非空和包含来定义的．由于缺乏对错误的容忍能力，其实际应用受到了限制．20世纪90年代初，Yao 等人结合贝叶斯决策理论提出了决策粗糙集模型．近年来，该模型逐渐得到重视，并在不确定性信息处理方面得到了广泛应用．该文首先就为什么要提出决策粗糙集模型、该模型具有什么特点以及该模型中需要解决的几个问题进行了详细讨论．然后，总结了国内外关于决策粗糙集模型的研究现状和进展，详细分析了存在的挑战性问题，并深入探讨了未来的研究方向．%As the central concepts in rough set theory,the classical Pawlak lower and upper approximations are defined based on qualitative set-inclusion and non-empty overlapping relations,respectively.Consequently,the theory suffers from an intolerance of errors,which greatly restricts its real-world applications.To overcome this limitation,Yao and colleagues proposed a decision-theoretic rough sets (DTRS )model in early 1990s ’by introducing the Bayesian decision theory into rough sets.In recent years,the model has attracted much attention and has been applied in uncertain information processing.This paper aims at (1)presenting a survey of the motivations for introducing the DTRS model,the main features of the model,and the problems to be studied in the model,(2 )reviewing the fundamental results,state-of-art research,and challenges,and (3)pointing out future perspectives and potential research topics.