协调广义决策多尺度序信息系统的知识获取

doi:10.16451/j.cnki.issn1003-6059.202209003

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (730 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要决策多尺度信息系统是一类特殊的数据集,系统中的每个对象无论在条件属性集上还是在决策属性上都可取多个尺度的标记值,并且从细粒度标记属性值到粗粒度标记属性值有一个信息粒度变换.文中针对广义决策多尺度序信息系统的知识获取问题展开研究.首先,引入尺度选择概念,一个尺度选择对应一个单尺度的序决策系统,并将优势关系引入广义决策多尺度信息系统,给出在不同尺度选择下对象集的优势类和集合的下近似和上近似的定义及其性质.然后,在协调广义决策多尺度序信息系统中定义5种最优尺度选择的概念,证明实际上只有2种不同类型的最优尺度选择,即最优尺度选择、下近似最优尺度选择、信任最优尺度选择是等价的,而上近似最优尺度选择与似然最优尺度也是等价的.最后,给出协调广义决策多尺度序信息系统的辨识矩阵约简方法,并在最优尺度选择基础上给出蕴含在协调广义决策多尺度序信息系统中的序决策规则.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张嘉茹
	吴伟志
	杨烨

关键词 ：多尺度序信息系统, 尺度选择, 粗糙集, 粒计算, 知识获取

Abstract：A decision Multi-scale inforMation systeM is a sPecial tyPe of Multi-scale data set and each object under each attribute in the systeM froM either the condition attribute set or the decision attribute is rePresented by different scales at different levels of the granulations, holding a granular inforMation transforMation froM a finer to a coarser labelled value. To solve the ProbleM of knowledge acquisition in generalized decision Multi-scale ordered inforMation systeMs, the concePt of scale selection is firstly defined. Each scale selection is linked with a single-scale ordered decision systeM. DoMinance relations are also introduced into decision Multi-scale inforMation systeMs, rePresentations of inforMation granules as well as lower and uPPer aPProxiMations of sets under different scale selections are Presented, and their relationshiPs are exaMined. Then, five tyPes of oPtiMal scale selections in consistent generalized decision Multi-scale ordered inforMation systeMs are defined. It is Proved that there are indeed two different tyPes of oPtiMal scale selections. The notions of oPtiMal scale selection, lower aPProxiMation oPtiMal scale selection and belief oPtiMal scale selection are all equivalent, and a scale selection is uPPer aPProxiMation oPtiMal if and only if it is Plausibly oPtiMal. Finally, based on oPtiMal scale selections, a Method of discernibility Matrix attribute reduction and ordered decision rules hidden in consistent generalized decision Multi-scale ordered inforMation systeMs are exPlored.

Key words： Multi-scale Ordered InforMation SysteMs Scale Selection Rough Sets Granular CoMPuting Knowledge Acquisition

收稿日期: 2022-07-18

ZTFLH:

TP 18

基金资助:国家自然科学基金项目(No.61976194,62076221)资助

通讯作者: 吴伟志,博士,教授,主要研究方向为粗糙集、粒计算、数据挖掘、人工智能.E-mail:wuwz@zjou.edu.cn.

作者简介: 张嘉茹,硕士研究生,主要研究方向为粗糙集、粒计算.E-mail:1697628349@qq.com. 杨烨,硕士研究生,主要研究方向为粗糙集、粒计算.E-mail:1523562213@qq.com.

引用本文:

张嘉茹 , 吴伟志, 杨烨. 协调广义决策多尺度序信息系统的知识获取[J]. 模式识别与人工智能, 2022, 35(9): 789-804. ZHANG Jiaru, WU Weizhi, YANG Ye. Knowledge Acquisition for Consistent Generalized Decision Multi-scale Ordered InforMation SysteMs. Pattern Recognition and Artificial Intelligence, 2022, 35(9): 789-804.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202209003 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2022/V35/I9/789

[1] BARGIELA A, PEDRYCZ W. Granular CoMPuting: An Introduction. Berlin, GerMany: SPringer, 2002.
[2] LIN T Y. Granular CoMPuting: Structures, RePresentations, and APPlications // Proc of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular CoMPuting. Berlin, GerMany: SPringer, 2003: 16-24.
[3] YAO Y Y. Granular CoMPuting: Basic Issues and Possible Solutions // Proc of the 5th Joint Conference on CoMPuting and InforMation[C/OL]. [2022-06-12]. httP://www2.cs.uregina.ca/~yyao/PAPERS/basic.Pdf.
[4] 陈德刚,徐伟华,李金海,等.粒计算基础教程.北京:科学出版社, 2020.
　　(CHEN D G, XU W H, LI J H, et al. Basic Course of Granular CoMPuting. Beijing, China: Science Press, 2020.)
[5] 梁吉业,钱宇华,李德玉,等.大数据挖掘的粒计算理论与方法.中国科学(信息科学), 2015, 45(11): 1355-1369.
　　(LIANG J Y, QIAN Y H, LI D Y, et al. Theory and Method of Granular CoMPuting for Big Data Mining. Scientia Sinica(InforMationis), 2015, 45(11): 1355-1369.)
[6] CHEN C L P, ZHANG C Y. Data-Intensive APPlications, Challenges, Techniques and Technologies: A Survey on Big Data. InforMation Sciences, 2014, 275: 314-347.
[7] 苗夺谦,张清华,钱宇华,等.从人类智能到机器实现模型——粒计算理论与方法.智能系统学报, 2016, 11(6): 743-757.
　　(MIAO D Q, ZHANG Q H, QIAN Y H, et al. FroM HuMan Inte-lligence to Machine IMPleMentation Model: Theories and APPlications Based on Granular CoMPuting. CAAI Transactions on Intelligent SysteMs, 2016, 11(6): 743-757.)
[8] 徐计,王国胤,于洪.基于粒计算的大数据处理.计算机学报, 2015, 38(8): 1497-1517.
　　(XU J, WANG G Y, YU H. Review of Big Data Processing Based on Granular CoMPuting. Chinese Journal of CoMPuters, 2015, 38(8): 1497-1517.)
[9] 庞继芳,宋鹏,梁吉业.面向决策分析的多粒度计算模型与方法综述.模式识别与人工智能, 2021, 34(12): 1120-1130.
　　(PENG J F, SONG P, LIANG J Y. Review on Multi-granulation CoMPuting Models and Methods for Decision Analysis. Pattern Re-cognition and Artificial Intelligence, 2021, 34(12): 1120-1130.)
[10] PAWLAK Z. Rough Sets: Theoretical AsPects of Reasoning about Data. Boston, USA: Kluwer AcadeMic Publishers, 1991.
[11] WU W Z, LEUNG Y. Theory and APPlications of Granular Labelled Partitions in Multi-scale Decision Tables. InforMation Sciences, 2011, 181(18): 3878-3897.
[12] LI F, HU B Q. A New APProach of OPtiMal Scale Selection to Multi-scale Decision Tables. InforMation Sciences, 2017, 381: 193-208.
[13] WU W Z, LEUNG Y. OPtiMal Scale Selection for Multi-scale Decision Tables. International Journal of APProxiMate Reasoning, 2013, 54(8): 1107-1129.
[14] 吴伟志,陈超君,李同军,等.不协调多粒度标记决策系统最优粒度的对比.模式识别与人工智能, 2016, 29(12): 1095-1103.
(WU W Z, CHEN C J, LI T J, et al. CoMParative Study on OPtiMal Granularities in Inconsistent Multi-granular Labeled Decision SysteMs. Pattern Recognition and Artificial Intelligence, 2016, 29(12): 1095-1103.)
[15] HAO C, LI J H, FAN M, et al. OPtiMal Scale Selection in DynaMic Multi-scale Decision Tables Based on Sequential Three-Way Decisions. InforMation Sciences, 2017, 415/416: 213-232.
[16] XIE J P, YANG M H, LI J H, et al. Rule Acquisition and OPtiMal Scale Selection in Multi-scale ForMal Decision Contexts and Their APPlications to SMart City. Future Generation CoMPuter SysteMs, 2018, 83: 564-581.
[17] CHENG Y L, ZHANG Q H, WANG G Y, et al. OPtiMal Scale Selection and Attribute Reduction in Multi-scale Decision Tables Based on Three-Way Decision. InforMation Sciences, 2020, 541: 36-59.
[18] ZHANG X Q, ZHANG Q H, CHENG Y L, et al. OPtiMal Scale Selection by Integrating Uncertainty and Cost-Sensitive Learning in Multi-scale Decision Tables. International Journal of Machine Learning and Cybernetics, 2020, 11(5): 1095-1114.
[19] 陈应生,李进金,林荣德,等.多尺度覆盖决策信息系统的布尔矩阵方法.模式识别与人工智能, 2020, 33(9): 776-785.
(CHEN Y S, LI J J, LIN R D, et al. Boolean Matrix APProach for Multi-scale Covering Decision InforMation SysteM. Pattern Recognition and Artificial Intelligence, 2020, 33(9): 776-785.)
[20] ZHENG J W, WU W Z, BAO H, et al. Evidence Theory Based OPtiMal Scale Selection for Multi-scale Ordered Decision SysteMs. International Journal of Machine Learning and Cybernetics, 2022, 13(4): 1115-1129.
[21] HUANG B, LI H X, FENG G F, et al. Double-Quantitative Rough Sets, OPtiMal Scale Selection and Reduction in Multi-scale DoMinance IF Decision Tables. International Journal of APProxiMate Reasoning, 2021, 130: 170-191.
[22] LI F, HU B Q, WANG J. StePwise OPtiMal Scale Selection for Multi-scale Decision Tables via Attribute Significance. Knowledge-Based SysteMs, 2017, 129: 4-16.
[23] 吴伟志,庄宇斌,谭安辉,等.不协调广义多尺度决策系统的尺度组合.模式识别与人工智能, 2018, 31(6): 485-494.
(WU W Z, ZHUANG Y B, TAN A H, et al. Scale CoMbinations in Inconsistent Generalized Multi-scale Decision SysteMs. Pattern Recognition and Artificial Intelligence, 2018, 31(6): 485-494.)
[24] WU W Z, LEUNG Y. A CoMParison Study of OPtiMal Scale CoMbination Selection in Generalized Multi-scale Decision Tables. In-ternational Journal of Machine Learning and Cybernetics, 2020, 11(5): 961-972.
[25] BAO H, WU W Z, ZHENG J W, et al. EntroPy Based OPtiMal Scale CoMbination Selection for Generalized Multi-scale InforMation Tables. International Journal of Machine Learning and Cyberne-tics, 2021, 12: 1427-1437.
[26] SHE Y H, ZHAO Z J, HU M T, et al. On Selection of OPtiMal Cuts in CoMPlete Multi-scale Decision Tables. Artificial Intelligence Review, 2021, 54: 6125-6148.
[27] 吴伟志,孙钰,王霞,等.不协调广义多尺度决策系统的局部最优尺度组合选择.模式识别与人工智能, 2021, 34(8): 689-700.
(WU W Z, SUN Y, WANG X, et al. Local OPtiMal Scale CoMbination Selections in Inconsistent Generalized Multi-scale Decision SysteMs. Pattern Recognition and Artificial Intelligence, 2021, 34(8): 689-700.)
[28] 张清华,张雪秋,庞国弘.多尺度决策系统中代价敏感的最优尺度组合.控制与决策, 2021, 36(10): 2369-2378.
(ZHANG Q H, ZHANG X Q, PANG G H. Cost-Sensitive OPtiMal Scale CoMbination in Multi-scale Decision SysteMs. Control and Decision, 2021, 36(10): 2369-2378.)
[29] ZHU Y J, YANG B. OPtiMal Scale CoMbination Selection for In-consistent Multi-scale Decision Tables. Soft CoMPuting, 2022, 26(13): 6119-6129.
[30] ZHANG Q H, CHENG Y L, ZHAO F, et al. OPtiMal Scale CoMbination Selection Integrating Three-Way Decision with Hasse DiagraM. IEEE Transactions on Neural Networks and Learning SysteMs, 2022, 33(8): 3675-3689.
[31] HUANG Z H, LI J J, DAI W Z, et al. Generalized Multi-scale Decision Tables with Multi-scale Decision Attributes. International Journal of APProxiMate Reasoning, 2019, 115: 194-208.
[32] SHAFER G. A MatheMatical Theory of Evidence. Princeton, USA: Princeton University Press, 1976.
[33] YAO Y Y, LINGRAS P J. InterPretations of Belief Functions in the Theory of Rough Sets. InforMation Sciences, 1998, 104(1/2): 81-106.
[34] WU W Z, LEUNG Y, ZHANG W X. Connections between Rough Set Theory and DeMPster-Shafer Theory of Evidence. International Journal of General SysteMs. 2002, 31(4): 405-430.
[35] WU W Z, LEUNG Y, MI J S. On Generalized Fuzzy Belief Functions in Infinite SPaces. IEEE Transactions on Fuzzy SysteMs, 2009, 17(2): 385-397.
[36] CAMPAGNER A, CIUCCI D, DENCEUX T. Belief Functions and Rough Sets: Survey and New Insights. International Journal of APProxiMate Reasoning, 2022, 143: 192-215.
[37] GRECO S, MATARAZZO B, SLOWINSKI R. Rough APProxiMation of a Preference Relation by DoMinance Relations. EuroPean Journal of OPerational Research, 1999, 117(1): 63-83.
[38] GRECO S, MATARAZZO B, SLOWINSKI R. Rough Sets Theory for Multicriteria Decision Analysis. EuroPean Journal of OPeratio-nal Research, 2001, 129(1): 1-47.
[39] GRECO S, MATARAZZO B, SLOWINSKI R. Rough APProxiMation by DoMinance Relations. International Journal of Intelligent SysteMs, 2002, 17(2): 153-171.
[40] SHAO M W, ZHANG W X. DoMinance Relation and Rules in an IncoMPlete Ordered InforMation SysteM. InforMational Journal of Intelligent SysteMs, 2005, 20(1): 13-27.
[41] ZHANG X X, CHEN D G, TSANG E C C. Generalized DoMinance Rough Set Models for the DoMinance Intuitionistic Fuzzy InforMation SysteMs. InforMation Sciences, 2017, 378: 1-25.
[42] HUANG B, LI H X, FENG G F, et al. DoMinance-Based Rough Sets in Multi-scale Intuitionistic Fuzzy Decision Tables. APPlied MatheMatics and CoMPutation, 2019, 348: 487-512.
[43] 徐伟华.序信息系统与粗糙集.北京:科学出版社, 2013.
(XU W H. Ordered InforMation SysteMs and Rough Sets. Beijing, China: Science Press, 2013.)
[44] XU W H, ZHANG X Y, ZHONG J M, et al. Attribute Reduction in Ordered InforMation SysteMs Based on Evidence Theory. Know-ledge and InforMation SysteMs, 2010, 25(1): 169-184.
[45] DU W S, HU B Q. Attribute Reduction in Ordered Decision Tables via Evidence Theory. InforMation Sciences, 2016, 364/365: 91-110.
[46] ZHAN J M, ZHANG K, WU W Z. An Investigation on Wu-Leung Multi-scale InforMation SysteMs and Multi-exPert GrouP Decision-Making. ExPert SysteMs with APPlications, 2021, 170. DOI: 10.1016/j.eswa.2020.114542.
[47] HUANG X F, ZHAN J M. TWD-R: A Three-Way Decision APProach Based on Regret Theory in Multi-scale Decision InforMation SysteMs. InforMation Sciences, 2021, 581: 711-739.
[48] DENG J, ZHAN J M, WU W Z. A Ranking Method with a Prefe-rence Relation Based on the PTOMETHEE Method in IncoMPlete Multi-scale InforMation SysteMs. InforMation Sciences, 2022, 608: 1261-1282.