基于最大粒的规则获取算法

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摘要粒计算是模拟人类思维和解决复杂问题的方法，它是复杂问题求解、海量数据挖掘、模糊信息处理的有效工具。文中首先分析并指出传统的规则获取方法存在的某些弊端，并从粒计算的角度分析属性约简的粒度原理，指出属性约简过程的本质是寻找决策划分空间的一个极大近似划分空间，而在极大近似划分空间上提取的规则可能不是最简规则。为此，提出一种基于最大粒的规则获取算法，该算法根据条件属性对论域形成的分层递阶的划分空间，自顶向下逐渐提取最大粒对应的规则。仿真实验表明该算法提高粗糙集的泛化能力。

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	张清华
	王国胤
	刘显全

关键词 ：属性约简, 知识获取, 粒计算, 知识空间, 商空间

Abstract：Granular computing (GrC) is a method for simulating human thinking and solving complicated problems. It is a powerful tool for solving complicated problems, mining massive data sets, and dealing with fuzzy information. In this paper, the shortcoming of the traditional rule extraction methods is presented, and then the granularity principle of rule extraction is analyzed based on granular computing method. The essence of attribute reduction is to choose a maximum approximation partition space of decision-making knowledge space, and the rules acquired from maximum approximation partition space may not be the simplest. Therefore, a rule extraction algorithm based on granular computing is proposed. In the proposed algorithm, the rules based on maximal granule can be acquired from information system in a hierarchical knowledge space in top-down manner, and the results of the simulation experiments illustrate that the generalization ability of rough set method is improved.

Key words： Attribution Reduction Knowledge Acquisition Granular Computing Knowledge Space Quotient Space

收稿日期: 2010-12-08

ZTFLH:

TP181

基金资助:国家自然科学基金项目(No.61073146,61075019)、重庆市教育科学研究项目(No.KJ110512)资助

作者简介: 张清华，男，1974年生，博士，副教授，主要研究方向为智能信息处理、粒计算等。E-mail:zhangqh@cqupt。edu。cn。王国胤，男，1970年生，教授，博士生导师，主要研究方向为Rough集理论、粒计算、数据挖掘、知识技术等。刘显全，男，1974年生，博士研究生，主要研究方向为知识发现、粒计算等。

引用本文:

张清华，王国胤，刘显全. 基于最大粒的规则获取算法[J]. 模式识别与人工智能, 2012, 25(3): 388-396. ZHANG Qing-Hua, WANG Guo-Yin, LIU Xian-Quan. Rule Acquisition Algorithm Based on Maximal Granule. , 2012, 25(3): 388-396.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2012/V25/I3/388

[1] Miao Duoqian,Wang Guoyin,Liu Qing,et al.Granular Computing: Past,Present and Future Prospects.Beijing,China: Science Press,2007 (in Chinese)
(苗夺谦,王国胤,刘清,等.粒计算:过去、现在与展望.北京:科学出版社,2007)
[2] Pawlak Z.Rough Sets.International Journal of Computer and Information Science,1982,11(5): 341-356
[3] Wang Guoyin.Rough Set Theory and Knowledge Discovery.Xian,China: Xian Jiaotong University Press,2001 (in Chinese)
(王国胤.Rough集理论与知识获取.西安:西安交通大学出版社,2001)
[4] Wong S K,Ziarko W.On Optimal Decision Rules in Decision Tables.Bulletin of Polish Academy of Sciences,1985,33(11): 693-696
[5] Lu Ruqian.Artificial Intelligence.Beijing,China: Science Press,1996 (in Chinese)
(陆汝钤.人工智能.北京:科学出版社,1996)
[6] Miao Duoqian,Hu Guirong.A Heuristic Algorithm for Reduction of Knowledge.Journal of Computer Research and Development,1999,36(6): 681-684 (in Chinese)
(苗夺谦,胡桂荣.知识约简的一种启发式算法.计算机研究与发展,1999,36(6): 681-684)
[7] Zhang Ling,Zhang Bo.The Theory and Applications of Problem Solving-Quotient Space Based Granular Computing.2nd Edition.Beijing,China: Tsinghua University Press,2007 (in Chinese)
(张铃,张钹.问题求解理论及应用——商空间粒度计算理论及其应用.第2版.北京:清华大学出版社,2007)
[8] Zhang Ling,Zhang Bo.A Quotient Space Approximation Model of Multi-Resolution Signal Analysis.Journal of Computer Science and Technology,2005,20(1):90-94
[9] Miao Duoqian,Wang Jue.An Information Representation of the Concepts and Operations in Rough Set Theory.Journal of Software,1999,10(2): 113-116 (in Chinese)
(苗夺谦,王珏.粗糙集理论中概念与运算的信息表示.软件学报,1999,10(2):113-116)
[10] Wang Guoyin,Yu Hong,Yang Dachun.Decision Table Reduction Based on Conditional Information Entropy.Chinese Journal of Computers,2003,25(7): 759-766 (in Chinese)
(王国胤,于洪,杨大春.基于条件信息熵的决策表约简.计算机学报,2003,25(7): 759-766)
[11] Wang Guoyin,Miao Duoqian,Wu Weizhi,et al.Uncertain Knowledge Representation and Processing Based on Rough Set.Journal of Chongqing University of Posts and Telecommunications: Natural Science Edition,2010,22(5): 541-544(in Chinese)
(王国胤,苗夺谦,吴伟志,等.不确定信息的粗糙集表示和处理.重庆邮电大学学报:自然科学版,2010,22(5): 541-544)
[12] Hu Xiaohua,Cercone N.Learning in Relational Databases: A Rough Set Approach.Computational Intelligence,1995,11(2): 323-338
[13] Ye Dongyi.An Improvement to Jeloneks Attribute Reduction Algorithm.Acta Electronica Sinica,2000,28(12): 81-82 (in Chinese)
(叶东毅.Jelonek属性约简算法的一个改进.电子学报,2000,28(12): 81-82)
[14] Pawlak Z.Rough Sets: Theoretical Aspects of Reasoning about Data.Dordrecht,Netherlands: KIuwer Academic Publishers,199l
[15] Zhang Wenxiu,Yao Yiyu,Leung Y.Rough Set and Concept Lattice.Xian,China: Xian Jiaotong University Press,2006 (in Chinese)
(张文修,姚一豫,梁怡.粗糙集与概念格.西安:西安交通大学出版社,2006)
[16] Wang Guoyin,Zhang Qinghua.Uncertainty of Rough Sets in Different Knowledge Granularities.Chinese Journal of Computers,2008,31(9): 1588-1598 (in Chinese)
(王国胤,张清华.不同知识粒度下粗糙集的不确定性研究.计算机学报,2008,31(9): 1588-1598)
[17] Wang Guoyin,Zhao Jun,An Jiujiang,et al.A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction.Fundamenta Informaticae,2005,68(3): 289-301
[18] An Jiujiang,Wang Guoyin,Wu Yu,et al.A Rule Generation Algorithm Based on Granular Computing // Proc of the IEEE International Conference on Granular Computing.Hong Kong,China,2005: 102-107
[19] Skowron A,Rauszer C.The Discernibility Matrices and Functions in Information Systems.Dordrecht,Netherlands: Kluwer Academic Publishers,1992: 331-362
[20] Wang Guoyin,Zheng Zheng,Zhang Yi.RIDAS-A Rough Set Based Intelligent Data Analysis System // Proc of the 1st International Conference on Machine Learning and Cybernetics.Beijing,China,2002: 646-649
[21] Zhang Qinghua,Wang Guoyin,Lin Xianquan.Analysis of the Hierarchical Structure of Fuzzy Quotient Space.Pattern Recognition and Artificial Intelligence,2008,21(5): 627-634 (in Chinese)
(张清华,王国胤,刘显全.分层递阶的模糊商空间结构分析.模式识别与人工智能,2008,21(5): 627-634)