摘要
阴影集(Shadowed set,SS)是一种对模糊集进行三支近似处理的不确定性知识发现模型,其能够对模糊集中具有精确值的不确定性对象进行有效的近似和划分,从而减少模糊决策过程中不确定性对象的决策划分成本和计算损耗.首先,回顾阴影集的发展历程,并从四个方面介绍其研究现状及内容,即阴影集的模型构建、理论性质、数据分析以及应用研究.通过总结分析它们的核心思想、方法体系、相互关系和区别等,为该领域的后续研究提供借鉴.随后,讨论分析阴影集理论与其他不确定性问题处理理论模型的联系,尤其是阴影集与模糊集、粗糙集和三支决策理论之间的区别、联系以及互补性.最后,围绕上述四个研究方面,对当前若干具有挑战性的研究问题进行分析和展望.
Abstract
Shadowed set(SS)is a kind of uncertain knowledge discovery model which carries out three-way approx-imate processing on fuzzy sets.It can effectively approximate and partition the uncertain objects with precise val-ues in fuzzy sets,so as to reduce the decision partitioning cost and calculation loss of uncertain objects in fuzzy de-cision-making process.Firstly,the development of shadowed set is reviewed,and introduces its research status and content from four aspects:Model construction,theoretical properties,data analysis and application research.By summarizing and analyzing their core ideas,methodological systems,interrelationships,and differences,etc.,this paper provides reference for subsequent research in this field.Subsequently,the connection between shadowed set theory and other uncertainty problem handling theoretical models is discussed and analyzed,especially the differ-ences,connections and complementarities between shadowed set and fuzzy sets,rough sets,and three-way decision theories.Finally,based on the above four research aspects,some current challenging research problems are ana-lyzed and prospected.
基金项目
国家重点研发计划(2021YFF0704101)
国家自然科学基金(62276038)
国家自然科学基金(62221005)
重庆市创新群体研究项目(cstc2019jcyjcxttX0002)
重庆邮电大学博士人才培养计划(BYJS202109)
重庆市教委重点合作项目(HZ2021008)
维普
万方数据