[1]车晓雅,李磊军,米据生.基于证据理论刻画多粒度覆盖粗糙集的数值属性[J].智能系统学报,2016,11(4):481-486.[doi:10.11992/tis.201606011]
 CHE Xiaoya,LI Leijun,MI Jusheng.Evidence-theory-based numerical characterization of multi-granulation covering rough sets[J].CAAI Transactions on Intelligent Systems,2016,11(4):481-486.[doi:10.11992/tis.201606011]
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基于证据理论刻画多粒度覆盖粗糙集的数值属性(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年4期
页码:
481-486
栏目:
出版日期:
2016-07-25

文章信息/Info

Title:
Evidence-theory-based numerical characterization of multi-granulation covering rough sets
作者:
车晓雅1 李磊军12 米据生12
1. 河北师范大学 数学与信息科学学院, 河北 石家庄 050024;
2. 河北省计算数学与应用重点实验室, 河北 石家庄 050024
Author(s):
CHE Xiaoya1 LI Leijun12 MI Jusheng12
1. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, China;
2. Hebei Key Laboratory of Computational Mathematics and Applications, Shijiazhuang 050024, China
关键词:
粗糙集理论覆盖粒度证据理论近似特性描述
Keywords:
rough sets theorycoveringgranulationevidence theoryapproximationcharacterization
分类号:
TP18
DOI:
10.11992/tis.201606011
摘要:
在经典多粒度粗糙集模型的基础上,基于论域中对象的极大描述和极小描述,定义了4种应用更为广泛的悲观多粒度覆盖粗糙集模型。然后通过集合的交、并运算与关系划分函数,构造了对象关于覆盖族的单粒度的多元覆盖及单粒度划分。在此基础上,基于证据理论,探讨了4种悲观多粒度覆盖粗糙集的上、下近似与信任函数和似然函数之间关系,并描述了该模型所具备的相关数值属性。对比分析表明悲观多粒度覆盖粗糙集模型既具备经典多粒度粗糙集模型能够融合多源信息的优势,又克服了其应用范围狭窄的缺点。实例分析验证了所提模型的有效性。
Abstract:
Considering classical multi-granulation rough sets and using the maximal and minimal descriptors of objects in a given universe, this paper proposes four pessimistic multi-granulation covering rough set models, suitable for extensive application. Based on set union and portion functions, the notion of multi-granularity covering connected to a number of coverings and a single granularity partition in the domain are defined. On this basis, belief and plausibility functions from evidence theory are employed to define the relationship between the upper and lower approximations, the belief function, and the likelihood function, and to characterize the set approximations in the four models. Compared with classical multi-granulation rough sets, the pessimistic multi-granulation covering rough set models not only have distinct advantages and combine multi-source information, but also avoid the shortcomings of a narrow application range. Finally, a real example is used to demonstrate the effectiveness of the presented models.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-06-03。
基金项目:国家自然科学基金项目(61573127,61502144,61300121,6147 2463);河北省自然科学基金项目(A2014205157);河北省高校创新团队领军人才培育计划项目(LJRC022);河北省高校自然科学基金项目(QN2016133);河北师范大学博士科学基金项目(L2015B01);河北省教育厅研究生创新项目(sj2015001).
作者简介:车晓雅,女,1991年生,硕士研究生,主要研究方向为人工智能的数学基础;李磊军,男,1985年生,讲师,博士,主要研究方向为粗糙集,概念格,粒计算与集成学习等,已发表学术论文10余篇,其中被SCI检索5篇。
通讯作者:车晓雅.E-mail:chexiaoya@163.com.
更新日期/Last Update: 1900-01-01