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2. College of Mathematics and Information Science, Guangxi University, Nanning, 530004, China)
"],"fundList_cn":["国家自然科学基金(71361002)，广西自然科学基金(桂科自019016), 广西高等学校立项科研项目( 2013LX095YB2014501 )
"],"article":{"juan":"51","zhaiyao_cn":"属性约简是粗糙集理论研究的重要内容之一.本文借助于属性集值的相似程度在集值信息系统上定义了一种新的相似度，用于度量知识的粗糙性，分析相关性质.在此基础上，提出一种基于相似度的启发式属性约简算法，以属性相似度为启发式信息，不需首先求核，尤其对核属性个数少甚至无核的集值信息系统计算约简更加有效.

","endNoteUrl_en":"https://jns.nju.edu.cn/EN/article/getTxtFile.do?fileType=EndNote&id=519","reference":"[1] Pawlak Z. Rough sets. International Journal of Computer and Information Science, 1982, 11:341～356.
[2] 王学恩,韩崇韶,韩德强等. 粗糙集研究综述. 控制工程, 2013, 20(1): 1~8.
[3] 谭德坤. 模糊粗糙集在科技文档检索中的应用研究. 计算机仿真, 2011, 28(10): 168~172.
[4] 徐红升,张瑞玲. 变精度粗糙集在智能诊疗系统中的应用. 计算机应用与软件, 2013, 30(2): 98~102.
[5] 赵荣泳,李翠玲,高晓康等. 电子镇流器故障诊断的变精度粗糙集模型. 南京大学学报(自然科学), 2010, 46(5): 494~500.
[6] 刘盾,李天瑞,李华雄等. 粗糙集理论:基于三支决策视角. 南京大学学报(自然科学), 2013, 49(5):574~581.
[7] 程玉胜,江效尧,胡林生等. 基于粗糙集理论的协调集及其决策树构造. 南京大学学报(自然科学), 2012,46(8):790~796.
[8] 黄玉龙,王翰虎,陈梅. 基于粗糙集理论的KNN分类. 广西师范大学学报(自然科学),2007, 25(4):75-78.
[9] 刘东升,琚春华,陈威. 基于粗糙集和支持向量机的客户价值分类. 广西师范大学学报(自然科学), 2009, 27(1): 97~100.
[10] 陈子春,刘鹏惠, 秦克云. 集值信息系统基于优势关系下的知识约简. 计算机科学, 2009,36(12): 176~179.
[11] 王虹,张文修,李鸿儒. 集值信息系统的知识发现与约简. 计算机工程与应用, 2005, (6):37~38.
[12] 王燕,莫智文. 基于集对分析的多值信息系统粗集模型及其约简. 四川师范大学学报(自然科学), 2007, 30(3): 318~320.
[13] 刘萍,张忠林,赵晓菲. 一种知识距离的集值信息系统属性约简算法. 兰州交通大学学报, 2013, 32(1):107~110.
[14] 马建敏,朱朝晖. 集值信息系统的属性约简. 工程数学学报, 2010, 27(5):883~888.
[15] 陈子春,黄小刚. 基于模糊相容关系的集值决策表的相对约简. 西华大学学报, 2012, 31(4):31~36.
[16] 陈子春,秦克云. 集值信息系统在相容关系下的属性约简. 模糊系统与数学, 2009, 23(1):150~154.
[17] Guan J W, Bell D A, Guan Z. Matris computation for information systems. Information Sciences, 2001, 131:129~135.
[18] 宋笑雪,李鸿儒,张文修. 集值信息系统的知识约简和属性特征. 计算机工
","bibtexUrl_cn":"//www.sanmikaiseki.com/jns/CN/article/getTxtFile.do?fileType=BibTeX&id=519","abstractUrl_en":"https://jns.nju.edu.cn/EN/Y2015/V51/I2/384","qi":"2","id":519,"nian":2015,"bianHao":"201409065","zuoZheEn_L":"Liu Yingying1, Lv Yuejin2*","juanUrl_en":"https://jns.nju.edu.cn/EN/Y2015","shouCiFaBuRiQi":"2015-03-06","qiShiYe":"384","qiUrl_cn":"//www.sanmikaiseki.com/jns/CN/Y2015/V51/I2","lanMu_cn":"","pdfSize":"759737","zuoZhe_CN":"刘莹莹1，吕跃进2*","risUrl_cn":"//www.sanmikaiseki.com/jns/CN/article/getTxtFile.do?fileType=Ris&id=519","title_cn":"基于相似度的集值信息系统属性约简算法基于相似度的集值信息系统属性约简算法","jieShuYe":"389","endNoteUrl_cn":"//www.sanmikaiseki.com/jns/CN/article/getTxtFile.do?fileType=EndNote&id=519","zhaiyao_en":"As an important mathematic method to deal with knowledge fuzziness and knowledge uncertainty, rough sets theory has been paid more and more attention and widely used in various fields such as artificial intelligence, pattern recognition and classification, intelligent information processing. Attribute reduction is one of the important contents of the rough set theory. For set-valued information system attribute reduction, some scholars have conducted research and achieved some progress in recent years. Though attribute reduction algorithm have obtained many essential achievements, there are still some important issues needed to be further resolved, especially fast and accurate algorithm of attribute reduction, which is still the hot spot of current research. However, most studies are aimed at core information system. For no core information system, there is not yet a relatively efficient algorithm. Based on the similar degree of attribute values, a new method to compute the similarity of attributes is defined in set-valued information system, and it is used to measure the roughness of knowledge and analyze some corresponding properties. A new improved algorithm of attribute reduction based on knowledge of the similarity is proposed and it is especially effective to set-valued information system without core. For core set-valued information system, the algorithm of this paper can seek an attribute reduction; for no core set-valued information system, the algorithm does not need to find out a core, reducing algorithm to calculate the time complexity and improving the efficiency of algorithm. At last, we use several classical databases from UCI repository to test the algorithm. The validity of the algorithm is illustrated by theoretical analysis and experimental results. Although the space complexity of the algorithm presented in this paper is as same as the existing heuristic attribute reduction algorithm, the latter wastes additional time on the calculation of core.
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","hasPdf":"true"},"authorNotes_cn":["(广西大学电气工程学院,南宁,530004广西大学数学与信息科学学院,南宁,530004)"]}">

基于相似度的集值信息系统属性约简算法基于相似度的集值信息系统属性约简算法

刘莹莹1，吕跃进2*

南京大学学报(自然科学版)››2015, Vol. 51››Issue (2): 384-389.

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南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (2) : 384-389.

基于相似度的集值信息系统属性约简算法基于相似度的集值信息系统属性约简算法

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Attribute reduction in set-valued information system based on similarity

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{{article.zuoZheCn_L}}.{{article.title_cn}}[J]. {{journal.qiKanMingCheng_CN}}, 2015, 51(2): 384-389

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