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作者机构:Yulin Normal Univ Key Lab Complex Syst Optimizat & Big Data Proc Dept Guangxi Educ Yulin Guangxi Peoples R China Guangxi Univ Nationalities Sch Math & Phys Nanning Guangxi Peoples R China Guangxi Univ Nationalities Sch Artificial Intelligence Nanning 530006 Guangxi Peoples R China Yulin Normal Univ Sch Math & Stat Yulin Guangxi Peoples R China
出 版 物:《JOURNAL OF INTELLIGENT & FUZZY SYSTEMS》 (智能与模糊系统杂志)
年 卷 期:2021年第40卷第5期
页 面:8775-8792页
核心收录:
学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:National Natural Science Foundation of China Natural Science Foundation of Guangxi [2018GXNSFDA294003, 2018GXNSFAA294134] Guangxi Higher Education Institutions of China [ 52] Special Scientific Research Project of Young Innovative Talents in Guangxi [2019 AC20052] Key Laboratory of Software Engineering in Guangxi University for Nationalities [2018-18XJSY-03] Research Project of Institute of Big Data in Yulin [YJKY03] Engineering Project of Undergraduate Teaching Reform of Higher Education in Guangxi [2017JGA179]
主 题:Rough set theory IIVIS similarity degree theta-reduction theta-discernibility matrix theta-information entropy theta-significance algorithm
摘 要:Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, theta-reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction algorithms base on theta-discernibility matrix, theta-information entropy and theta-significance in an IIVIS are given.