[1]QU Guohua,LI Chunhua,ZHANG Qiang.Attribute reduction and discernibility function in factor space[J].CAAI Transactions on Intelligent Systems,2017,12(6):889-893.[doi:10.11992/tis.201609014]
Copy

Attribute reduction and discernibility function in factor space

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 6 Page number: 889-893 Column: 学术论文—人工智能基础 Public date: 2017-12-25
Title:
Attribute reduction and discernibility function in factor space
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
Keywords:
factor space; rough set; factor reduction; discernibility function; factorial causality analysis
CLC:
TP181
DOI:
10.11992/tis.201609014
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.
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.
Similar References:

Memo

-

Last Update: 2018-01-03

Copyright © CAAI Transactions on Intelligent Systems