计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (6): 131-141.doi: 10.11896/jsjkx.220800149

• 粒计算与知识发现 • 上一篇    下一篇

不协调广义决策多尺度序信息系统的最优尺度选择与规则提取

杨烨1, 吴伟志1,2, 张嘉茹1   

  1. 1 浙江海洋大学信息工程学院 浙江 舟山 316022
    2 浙江省海洋大数据挖掘与应用重点实验室(浙江海洋大学) 浙江 舟山 316022
  • 收稿日期:2022-08-15 修回日期:2022-11-28 出版日期:2023-06-15 发布日期:2023-06-06
  • 作者简介:(1523562213@qq.com)
  • 基金资助:
    国家自然科学基金(61976194,62076221)

Optimal Scale Selection and Rule Acquisition in Inconsistent Generalized Decision Multi-scale Ordered Information Systems

YANG Ye1, WU Weizhi1,2, ZHANG Jiaru1   

  1. 1 School of Information Engineering,Zhejiang Ocean University,Zhoushan,Zhejiang 316022,China
    2 Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province(Zhejiang Ocean University),Zhoushan,Zhejiang 316022,China
  • Received:2022-08-15 Revised:2022-11-28 Online:2023-06-15 Published:2023-06-06
  • About author:YANG Ye,born in 1999,postgraduate.Her main research interests include rough set and granular computing.WU Weizhi,born in 1964,Ph.D,professor.His main research interests include rough set,granular computing,data mining and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61976194,62076221).

PDF (PC)

252

摘要： 粒计算模拟人类思考问题的模式,在大数据挖掘和知识发现方面有独特优势。针对不协调的广义决策多尺度序信息系统的知识获取问题,利用证据理论来研究不协调的广义决策多尺度序信息系统的最优尺度选择与规则提取。首先,将优势关系引入决策多尺度信息系统中,并介绍广义决策多尺度序信息系统的相关概念;其次,通过引入不协调广义决策多尺度序信息系统的尺度组合概念,给出不同尺度组合下信息粒和集合的下近似与上近似的表示及其相互关系,并进一步定义了几种针对不同决策的不协调广义决策多尺度序信息系统的最优尺度组合概念,讨论了它们之间的关系;最后,给出了基于广义优势决策函数的辨识矩阵属性约简与规则提取方法。

关键词: 证据理论, 多尺度序信息系统, 尺度选择, 粗糙集, 粒计算

Abstract: Granular computing imitates human being's thinking.It shows great promise as a new way for data mining and know-ledge discovery in the context of big data.To solve the problem of knowledge acquisition in inconsistent generalized decision multi-scale ordered information systems,by employing evidence theory,the optimal scale combination and rule extraction in inconsistent generalized decision multi-scale ordered information systems are studied.Dominance relations are first introduced into decision multi-scale information systems,and some basic concepts in decision multi-scale ordered information systems are introduced.With reference to the notion of scale combinations in inconsistent generalized decision multi-scale ordered information systems,representations of information granules as well as lower and upper approximations of sets under different scale combinations are presented and their relationships are examined.With different scales of decisions,several types of optimal scale combinations in inconsistent generalized decision multi-scale ordered information systems are further defined and their relationships are clarified.Finally,a method of discernibility matrix attribute reduction and rule acquisition based on generalized dominance decision functions are explored.

Key words: Evidence theory, Multi-scale ordered information systems, Scale selection, Rough sets, Granular computing

中图分类号: 

  • TP182
[1]LIN T Y.Granular computing:From rough sets and neighborhood systems to information granulation and computing in words[C]//Proceedings of the European Congress on Intelligent Techniques and Soft Computing.1997.
[2]LIANG J Y,QIAN Y H,LI D Y,et al.Theory and method of granular computing for big data mining[J].Scientia Sinica Informationis,2015,45(11):1355-1369.
[3]CHEN C L P,ZHANG C Y.Data-intensive applications,challenges,techniques and technologies:A survey on Big Data[J].Information Sciences,2014,275:314-347.
[4]XU J,WANG G Y,YU H.Review of big data processing based on granular computing[J].Chinese Journal of Computers,2015,38(8):1497-1517.
[5]CHEN L F,DAI Q,FU Q F.Design and application of extreme learning machine model based on granular Computing[J].Computer Science,2018,45(10):59-63.
[6]PEDRYCZ W.Granular Computing:Analysis and Design of Intelligent Systems[M].Boca Raton:CRC Press,2013.
[7]PEDRYCZ W,SKOWRON A,KREINOVICH V.Handbook of Granular Computing[M].New York:Wiley,2008.
[8]PEDRYCZ W,SUCCI G,SILLITTI A,et al.Data description:A general framework of information granules[J].Knowledge-Based Systems,2015,80:98-108.
[9]WU W Z,LEUNG Y,MI J S.Granular computing and know-ledge reduction in formal contexts[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(10):1461-1474.
[10]YANG X B,SONG X N,CHEN Z H,et al.On multigranulation rough sets in incomplete information system[J].International Journal of Machine Learning and Cybernetics,2012,3:223-232.
[11]PAWLAK Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[12]PAWLAK Z,SKOWRON A.Rudiments of rough sets[J].Information Sciences,2007,177(1):3-27.
[13]ACHARJYA D P,ABRAHAM A.Rough computing—A review of abstraction,hybridization and extent of applications[J].Engineering Applications of Artificial Intelligence,2020,96:103924.
[14]FENG F,LI C X,DAVVAZ B,et al.Soft sets combined with fuzzy sets and rough sets:a tentative approach[J].Soft Computing,2010,14(9):899-911.
[15]LU Y L,LEI Y J,ZHOU W.Generalized intuitionistic fuzzy rough set model[J].Computer Science,2017,44(7):232-236.
[16]DUBOIS D,PRADE H.Rough fuzzy sets and fuzzy rough sets[J].International Journal of General System,1990,17(2/3):191-209.
[17]GRECO S,MATARAZZO B,SLOWINSKI R.A new rough set approach to evaluation of bankruptcy risk[M].Boston:Kluwer Academic Publishers,1998:121-136.
[18]GRECO S,MATARAZZO B,SLOWINSKI R.Rough approximation of a preference relation by dominance relations[J].European Journal of Operational Research,1999,117(1):63-83.
[19]GRECO S,MATARAZZO B,SLOWINSKI R.Rough sets theory for multicriteria decision analysis[J].European Journal of Operational Research,2001,129(1):1-47.
[20]GRECO S,MATARAZZO B,SLOWINSKI R.Rough approxi-mation by dominance relations[J].International Journal of Intelligent Systems,2002,17(2):153-171.
[21]GRECO S,MATARAZZO B,SŁOWINSKI R.Dominance-based rough set approach as a proper way of handling graduality in rough set theory[M]//Transactions on Rough Sets VII.Berlin:Springer,2007:36-52.
[22]XU W H.Ordered Information Systems and Rough Sets[M].Beijing:Science Press,2013.
[23]SANG B B,XU W H.Assignment reduction of intuitionisticfuzzy ordered decision information system[J].Computer Science,2017,44(6):75-79.
[24]FANG L H,LIN Y M,WU W Z.Optimal Scale Selection in Random Multi-scale Ordered Decision Systems[J].Computer Science,2022,49(6):172-179.
[25]QIAN Y H,DANG C Y,LIANG J Y,et al.Set-valued ordered information systems[J].Information Sciences,2009,179(16):2809-2832.
[26]ZHANG X X,CHEN D G,TSANG E C C.Generalized dominance rough set models for the dominance intuitionistic fuzzy information systems[J].Information Sciences,2017,378:1-25.
[27]HU C X,ZHANG L.Dynamic dominance-based multi granulation rough sets approaches with evolving ordered data[J].International Journal of Machine Learning and Cybernetics,2021,12(1):17-38.
[28]WU W Z,LEUNG Y.Theory and applications of granular labelled partitions in multi-scale decision tables[J].Information Sciences,2011,181(18):3878-3897.
[29]WU W Z,LEUNG Y.Optimal scale selection for multi-scale decision tables[J].International Journal of Approximate Reaso-ning,2013,54(8):1107-1129.
[30]LI F,HU B Q.A new approach of optimal scale selection tomulti-scale decision tables[J].Information Sciences,2017,381:193-208.
[31]WU W Z,LEUNG Y.A comparison study of optimal scale combination selection in generalized multi-scale decision tables[J].International Journal of Machine Learning and Cybernetics,2020,11:961-972.
[32]HUANG Z H,LI J J,DAI W Z,et al.Generalized multi-scale decision tables with multi-scale decision attributes[J].International Journal of Approximate Reasoning,2019,115:194-208.
[33]SHAFER G.A mathematical theory of evidence[M].Princeton:Princeton University Press,1976.
[34]YAO Y Y,LINGRAS P J.Interpretations of belief functions in the theory of rough sets[J].Information Sciences,1998,104(1/2):81-106.
[35]ZHENG J W,WU W Z,BAO H,et al.Evidence theory basedoptimal scale selection for multi-scale ordered decision systems[J].International Journal of Machine Learning and Cybernetics,2022,13(4):1115-1129.
[36]WEI B P,LV Y J,LI J H,et al.Attribute reduction and rule acquisition in incomplete and inconsistent ordered decision systems[J].Computer Science,2013,40(11):160-164.
[37]WU W Z,GAO C J,LI T J.Ordered granular labeled structures and rough approximations[J].Journal of Computer Research and Development,2014,51(12):2623-2632.
[38]SHAO M W,ZHANG W X.Dominance relation and rules in an incomplete ordered information system[J].International Journal of Intelligent Systems,2005,20(1):13-27.
[39]WU W Z.Attribute reduction based on evidence theory in in-complete decision systems[J].Information Sciences,2008,178(5):1355-1371.
[40]XU W H,ZHANG X Y,ZHONG J M,et al.Attribute reduction in ordered information systems based on evidence theory[J].Knowledge and Information Systems,2010,25(1):169-184.
[41]DU W S,HU B Q.Attribute reduction in ordered decision tables via evidence theory[J].Information Sciences,2016,364/365:91-110.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发