[1]曲国华,李春华,张强.因素空间中属性约简的区分函数[J].智能系统学报,2017,(06):889-893.[doi:10.11992/tis.201609014]
 QU Guohua,LI Chunhua,ZHANG Qiang.Attribute reduction and discernibility function in factor space[J].CAAI Transactions on Intelligent Systems,2017,(06):889-893.[doi:10.11992/tis.201609014]
点击复制

因素空间中属性约简的区分函数(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2017年06期
页码:
889-893
栏目:
出版日期:
2017-12-25

文章信息/Info

Title:
Attribute reduction and discernibility function in factor space
作者:
曲国华1 李春华1 张强2
1. 山西财经大学 管理科学与工程学院, 山西 太原 030006;
2. 北京理工大学 管理与经济学院, 北京 100081
Author(s):
QU Guohua1 LI Chunhua1 ZHANG Qiang2
1. School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China;
2. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
关键词:
因素空间粗糙集因素约简区分函数因果分析法
Keywords:
factor spacerough setfactor reductiondiscernibility functionfactorial causality analysis
分类号:
TP181
DOI:
10.11992/tis.201609014
摘要:
粗糙集用属性所构建的信息系统来描写事物,用各种细化的熵指标来实现信息的标度,为挖掘知识的关系数据库提供了数学基础,当前人们最关注的是她在属性约简中所能发挥的作用。但是它用以约简的区分函数定义不清楚,当没有属性能区分两个对象时,相应的属性变量为什么不取0而是取1?这一问题成为粗糙集应用的一个瓶颈。本文的目的是要为区分函数寻找更合理的解释和运用。所采用的方法是,首先要对属性名之间的运算要下定义,属性名与属性值不同,如果用属性值的运算来代替属性名的运算,就会在理解上出现混乱。为此,我们用因素空间的理论,将属性名视为因素,用因素之间的运算来定义属性名的运算,使区分函数有了明确的定义,同时也清楚解释了属性变量在特殊情况下为何取1的问题。这一结果说明因素空间可以加深粗糙集的理论基础,提高其解决问题的能力。
Abstract:
To enable description, Rough Set theory uses an information system constructed by attributes, and various detailed entropy indexes are employed to achieve the scale of information; this provides a mathematical basis for knowledge mining of relational databases. Current research is focused on the role that Rough Set plays in attribute reduction; however, definition of the discernibility function used for attribute reduction is unclear. For example, when there is no attribute to distinguish between two objects, it is unclear why 1 is used instead of 0 for the corresponding attribute variable. As such, this problem causes a bottleneck when applied in Rough Set. The aim of this paper is to find a more reasonable explanation and application for discernibility functions. The method firstly defines the operation between attribute names, which is different from the operation between attribute values, and the attribute name is different from the attribute value. If operation of the attribute value is confused with that of the attribute name, the meaning will subsequently be unclear. To avoid such confusion, Factor Space theory is employed, as it treats attribute names as factors. The theory uses the operation between factors to define the operation of the attribute name, enabling clear definition of the discernibility function, and explains why the attribute variable takes the value of 1 under special circumstances. Results indicate that Factor Space theory can deepen the theoretical basis of Rough Set and improve its ability to solve problems.

参考文献/References:

[1] PAWLAK Z. Rough sets[J]. International journal of computer and information sciences, 1982, 11(5): 341-356.
[2] WILLE R. Restructuring lattice theory: an approach based on hierarchies of concepts[M]. Ordered sets. Springer. 1982: 445-470.
[3] 汪培庄, SUGENO M. 因素场与模糊集的背景结构[J]. 模糊数学, 1982, (2):45-54.
WANG Peizhuang, SUGENO M. The factors field and background structure for fuzzy subsets[J]. Fuzzy mathematics, 1992(2): 45-54.
[4] POLKOWSKI S L, SKOWRON S A. Rough sets in knowledge discovery 2 [M]. Physica-Verlag HD, 1989.
[5] 叶东毅, 陈昭炯. 一个新的差别矩阵及其求核方法 [J]. 电子学报, 2002, 30(7): 1086-1088.
YE Dongyi, CHEN Zhaojiong. A new discernibiligy maatrix and the computation of a core[J]. Acta electornica sinica 2002, 30(7): 1086-1088.
[6] 王昊, 朱惠, 邓三鸿. 基于形式概念分析的学科术语层次关系构建研究[J]. 情报学报, 2015, (6): 616-627.
WANG Hao, ZHU Hui, DENG Sanhong. Study on construction of hierarchy relationship of subject terms based on formal concept analysis[J]. Journal of the China society for scientific andtechnical information, 2015, (6): 616-627.
[7] 李元诚, 方廷健. 一种基于粗糙集理论的SVM短期负荷预测方法[J]. 系统工程与电子技术, 2004, 26(2): 187-190.
LI Yuanchen, FANG Tingjian. Approach to forecast short-term load of SVM based on rough sets[J]. Systems engineering and electronics, 2004, 26(2): 187-190.
[8] 张腾飞, 肖健梅, 王锡淮. 粗糙集理论中属性相对约简算法 [J]. 电子学报, 2005, 33(11): 2080-2083.
ZHANG Tengfei, XIAO Jianmei, WANG Xihuai, et al. Algorithms of attribute relative reduction in rough set theory [J]. Acta electornica sinica, 2005, 33(11): 2080-2083.
[9] 戎晓霞, 刘家壮, 马英红. 基于Rough集的决策表属性最小约简的整数规划算法[J]. 计算机工程与应用, 2004, 40(11): 24-25.
RONG Xiaoxia, LIU Jiazhuang, MA Yinghong. Integer programming algorithm for finding minimal reduction in decision table based on rough set[J]. Computer engineering and application, 2004, 40(11): 24-25.
[10] XU Y, WANG L. Fault diagnosis system based on rough set theory and support vector machine[C]//Proceedings of the Fuzzy Systems and Knowledge Discovery, Second International Conference. Changsha, China.2005.
[11] 张文修. 粗糙集理论与方法 [M]. 北京:科学出版社, 2001.
ZHANG Wenxiu. Theory and method of rough set[M]. Beijing: Science Press, 2001.
[12] 汪培庄, 李洪兴. 知识表示的数学理论 [M]. 天津:天津科学技术出版社, 1994.
WANG Peizhuang, LI Hongxing. A mathematical theory on knowledge representation[M]. Tianjin: Tianjin Scientific and Technical Press, 1994.
[13] 汪培庄. 因素空间与因素库[J]. 辽宁工程技术大学学报: 自然科学版, 2013, 10: 1297-1304.
WANG Peizhuang. Factor spaces and factor data-bases[J]. Journal of Liaoning technical university: natural science, 2013, 32(10): 1-8.
[14] 汪培庄, 郭嗣琮, 包研科, 等. 因素空间中的因素分析法 [J]. 辽宁工程技术大学学报: 自然科学版, 2014, 33(7): 865-870.
WANG Peizhuang, GUO Sicong, BAO Yanke, et al. Factorial analysis in factor space[J]. Journal of Liaoning technical university: natural science, 2014, 33(7): 865-870.
[15] 刘海涛, 郭嗣琮. 因素分析法的推理模型[J]. 辽宁工程技术大学学报, 2015, 34(1): 124-128.
LIU Haitao, GUO Sicong. The reasoning model for factorial analysis[J]. Journal of Liaoning engineering technical university, 2015, 34(1): 124-128.

相似文献/References:

[1]尹林子,阳春华,桂卫华,等.规则分层约简算法[J].智能系统学报,2008,(06):492.
 YIN Lin-zi,YANG Chun-hua,GUI Wei-hua,et al.Hierarchical reduction of rules[J].CAAI Transactions on Intelligent Systems,2008,(06):492.
[2]毋 非,封化民,申晓晔.容错粗糙模型的事件检测研究[J].智能系统学报,2009,(02):112.
 WU Fei,FENG Hua-min,SHEN Xiao-ye.Research on event detection based on the tolerance rough set model[J].CAAI Transactions on Intelligent Systems,2009,(06):112.
[3]伞 冶,叶玉玲.粗糙集理论及其在智能系统中的应用[J].智能系统学报,2007,(02):40.
 SAN Ye,YE Yu-ling.Rough set theory and its application in the intelligent systems[J].CAAI Transactions on Intelligent Systems,2007,(06):40.
[4]王国胤,张清华,胡 军.粒计算研究综述[J].智能系统学报,2007,(06):8.
 WANG Guo-yin,ZHANG Qing-hua,HU Jun.An overview of granular computing[J].CAAI Transactions on Intelligent Systems,2007,(06):8.
[5]裴小兵,吴 涛,陆永忠.最小化决策规则集的计算方法[J].智能系统学报,2007,(06):65.
 PEI Xiao-bing,WU Tao,LU Yong-zhong.Calculating method for a minimal set of decision rules[J].CAAI Transactions on Intelligent Systems,2007,(06):65.
[6]张志飞,苗夺谦.基于粗糙集的文本分类特征选择算法[J].智能系统学报,2009,(05):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
 ZHANG Zhi-fei,MIAO Duo-qian.Feature selection for text categorization based on rough set[J].CAAI Transactions on Intelligent Systems,2009,(06):453.[doi:10.3969/j.issn.1673-4785.2009.05.011]
[7]马胜蓝,叶东毅.一种带禁忌搜索的粒子并行子群最小约简算法[J].智能系统学报,2011,(02):132.
 MA Shenglan,YE Dongyi.A minimum reduction algorithm based on parallel particle subswarm optimization with tabu search capability[J].CAAI Transactions on Intelligent Systems,2011,(06):132.
[8]顾成杰,张顺颐,杜安源.结合粗糙集和禁忌搜索的网络流量特征选择[J].智能系统学报,2011,(03):254.
 GU Chengjie,ZHANG Shunyi,DU Anyuan.Feature selection of network traffic using a rough set and tabu search[J].CAAI Transactions on Intelligent Systems,2011,(06):254.
[9]周丹晨.采用粒计算的属性权重确定方法[J].智能系统学报,2015,(02):273.[doi:10.3969/j.issn.1673-4785.201312008]
 ZHOU Danchen.A method for ascertaining the weight of attributes based on granular computing[J].CAAI Transactions on Intelligent Systems,2015,(06):273.[doi:10.3969/j.issn.1673-4785.201312008]
[10]陈坚,陈健,邵毅明,等.粗糙集的过饱和多交叉口协同优化模型研究[J].智能系统学报,2015,(5):783.[doi:10.11992/tis.201406045]
 CHEN Jian,CHEN Jian,SHAO Yiming,et al.Collaborative optimization model for oversaturated multiple intersections based on the rough set theory[J].CAAI Transactions on Intelligent Systems,2015,(06):783.[doi:10.11992/tis.201406045]

备注/Memo

备注/Memo:
收稿日期:2016-09-12;改回日期:。
基金项目:国家自然科学基金项目(71371030);山西省重点学科建设项目编号;山西财经大学青年科研基金项目(QN-2017007);山西省高等学校哲学社会科学研究项目(2017326).
作者简介:曲国华,男,1982年生,讲师,博士,主要研究方向为模糊决策、人工智能。先后主持山西省哲学社会科学1项,山西财经大学校青年基金项目1项,山西财经大学专项基金一项;参与国家自然科学基金3项,国家自然科学基金和高等学校博士学科点专项科研基金资助课题1项,北京市哲学社会科学规划项目1项,广东省软科学项目1项,广东省自然科学基金项目1项,广东省哲学社科十二五规划项目1项,广东省教育厅科技创新项目1项,广州市哲学社科十二五规划项目1项。发表学术论文15余篇;李春华,女,1988年生,硕士研究生,主要研究方向为模糊决策、环境与资源保护法。近3年参与国家自然科学基金1项,国家社会科学基金1项,山西省哲学社会科学1项,发表学术论文5篇;张强,1955年生,教授,博士生导师,主要研究方向为管理决策、对策论(博弈论)、模糊集理论与应用、非可加测度论、物流与供应链管理、智能算法、城市交通网络平衡分析。先后主持与参加科研项目10项,其中国家自然科学基金项目6项,发表学术论文400余篇,其中80篇被SCI检索,40篇被EI检索。
通讯作者:曲国华.E-mail:xz_qgh@163.com.
更新日期/Last Update: 2018-01-03