Computer Science ›› 2018, Vol. 45 ›› Issue (10): 54-58.doi: 10.11896/j.issn.1002-137X.2018.10.011

• CGCKD 2018 • Previous Articles     Next Articles

Generalized Dominance-based Attribute Reduction for Multigranulation Intuitionistic Fuzzy Rough Set

LIANG Mei-she1,2, MI Ju-sheng1, FENG Tao3   

  1. College of Mathematics and Information Science,Hebei Normal University,Shijiazhuang 050024,China 1
    Department of Science,Technology and School-Business Cooperation,Shijiazhuang University of Applied Technology,Shijiazhuang 050081,China 2
    College of Science,Hebei University of Science & Technology,Shijiazhuang 050018,China 3
  • Received:2018-04-17 Online:2018-11-05 Published:2018-11-05

Abstract: The combination of the evidence theory and multigranulation rough set model has become one of the hot issues,and the established models have been applied to various information systems,such as incomplete information system,coverage information system and fuzzy information system.However,intuitionistic fuzzy information system has not been investigated yet.Firstly,three kinds of dominance relations and three kinds of dominance classes were defined by using triangular norms and triangular conorms in intuitionistic fuzzy decision information system.Secondly,generali-zed dominance-based multigranulation intuitionistic fuzzy rough set model was proposed,and the belief structure of this model was discussed under evidence theory.After that,attribute reduction was acquired by the importance of granularity and attribute.Finally,an example was used to illustrate the effectiveness of the model.

Key words: Dominance relation, Evidence theory, Intuitionistic fuzzy set, Multigranulation, Rough set, Triangular norm

CLC Number: 

  • TP391
[1]PAWLAK Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[2]PAWLAK Z.Rough sets:some extensions[J].Information Scien- ces,2007,177(1):3-27.
[3]QIAN Y H,LIANG J Y.Rough set method based on multi- ganulations[C]∥Proceedings of the 5th IEEE International Conference on Cognitive Informatics.Piscataway,2006:297-304.
[4]QIAN Y H,LIANG J Y,YAO Y Y,et al.MGRS:a multi-gra- nulati on rough set[J].Information Sciences,2010,180(6):949-970.
[5]QIAN Y H,LIANG J Y,WEI W.Pessimistic rough decision [J].Journal of Zhe Jiang Ocean University(Natural Science),2010,29(5):440-449.
[6]QIAN Y H,LIANG J Y,DANG C Y.Incomplete multi-granulation rough set[J].IEEE transactions on Systems,Man and Cybernetics,Part A:systems and humans,2010,40(2):420-431.
[7]XU W H,WANG Q R,ZHANG X T.Multi-granulation rough sets based on tolerance relations[J].Soft Computing,2013,17(7):1241-1252.
[8]XU W H,GUO Y T.Generalized multigranulation double-quantitative decision-theoretic rough set[J].Knowledge-Based Systems,2016,105(1):190-205.
[9]ZHANG M,XU W Y,YANG X B,et al.Incomplete variable multigranulation rough sets decision[J].Applied Mathematics and Information Sciences,2014,8(3):1159-1166.
[10]XU W H,SUN W X,ZHANG X Y,et al.Multiple granulation rough set approach to ordered information systems[J].International Journal of General Systems,2012,41(5):475-501.
[11]YANG X B,SONG X N,DOU H L,et al.Multi-granulation rough set:from crisp to fuzzy case[J].Analysis of Fuzzy Mathematics and Informatics,2011,1(1):55-70.
[12]LIN G P,QIAN Y H,LI J Y.NMGRS:neighbor-hood-based multi-granulation rough sets[J].International Journal of Approximate Reasonining,2012,53(7):1080-1093.
[13]QIAN Y H,ZHANG H,SANG Y L,et al.Multi-granulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[14]GRECO S,MATARAZZO B,SLOWINSKI R.Rough sets theory for multicriteria decision analysis[J].European Journal of Operational Research,2001,129(1):1-47.
[15]GRECO S,MATARAZZO B,SLOWINSKI R.Fuzzy rough sets and multiple-premise gradual decision rules[J].International Journal of Approximate Reasoning,2006,41(2):179-211.
[16]JIAN L R,TANG X W,LIU S F,et al.Process Evaluation of Construction Project Based on Dominance Rough Set Approach[J].Journal of Systems & Management,2009,18(5):577-582.
[17]HUANG B,HU Z J,ZHOU X Z.Dominance relation-based fuzzy-rough model and its application to audit risk evaluation[J].Control and Decision,2009,24(6):899-902.(in Chinese)
黄兵,胡作进,周献中.优势模糊粗糙模型及其在审计风险评估中的应用[J].控制与决策,2009,24(6):899-902.
[18]AN L,CHEN Z,TONG L.Generation and application of decision rules within Dominance-based Rough Set Approach to multicriteria sorting[J].International Journal of Innovative Computing Information & Control Ijicic,2011,7(3):1145-1155.[19]ZADEH L A.Fuzzy sets[J].Information and Control,1965(8):338-353.
[20]ATANASSOV K T.Intuitionistic fuzzy sets[M].Heidelberg,German:Springer-Verlag Telos,1999:1-324.
[21]ATANASSOV K T.Intuitionistic fuzzy sets[J].Fuzzy Sets System,1986,20(1):87-96.
[22]ATANASSOV K T.New operations defined over the intuitionistic fuzzy sets[J].Fuzzy Sets System,1994,61(2):137-142.
[23]SHAFER G.A Mathematical theory of evidence [M].Prince- ton:Princeton University Press,1976.
[24]WU W Z,LEUNG Y,MI J S.On generalized fuzzy belief functions in infinite spaces[J].IEEE Transactions on Fuzzy Systems,2009,17:385-397.
[25]YAO Y Y,WONG S K M,LINGRAS P.A decision theoretic rough set model[C]∥Proceedings of the 5th International Symposium on Methodologies for Intelligent System.New York:North-Holland,1990:17-24.
[26]TAN A H,et al.Evidence-theory-based numerical characterization of multigranulation rough sets in incomplete information systems[J].Fuzzy Sets and Systems,2015,294(C):18-35.
[27]CHEN D G,LI W L,ZHANG X,et al.Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets[J].International Journal of Approximate Reasoning,2014,55(3):908-923.
[28]CHE X Y,MI J S,CHEN D G.Information fusion and numerical characterization of a multi-source information system[J].Knowledge-Based Systems,2018(145):121-133.
[29]HU Q,MI J S,LI L J.The fuzzy belief structure and attribute reduction based on multi-granulation fuzzy rough operator[J].Journal of Shandong University,2017,52(7):30-36.(in Chinese)
胡谦,米据生,李磊军.多粒度模糊粗糙近似算子的信任结构与属性约简[J].山东大学学报(理学版),2017,52(7):30-36.
[30]XU Z S.Intuitionistic preference relations and their application in group decision making[J].Information Sciences,2007,177(11):2363-2379.
[31]KLIR G J,YUAN B.Fuzzy sets and Fuzzy logic:Theory and Applications[M].Prentice Hallof India,2008.
[32]ZHANG X,CHEN D,TSANGC E C C.Generalized dominane rough set models for the dominance intuitionistic fuzzy information systems[J].Information Sciences,2017,378:1-25.
[33]LIANG M S,MI J S,ZHAO T N.Generalized dominance-based multi-granularity intuitionistic fuzzy rough set and acquisition of decision rules[J].CAAI Transactions on Intelligent Systems,2017,12(6):883-888.(in Chinese)
梁美社,米据生,赵天娜.广义优势多粒度直觉模糊粗糙集及规则获取[J].智能系统学报,2017,12(6):883-888.
[1] CHENG Fu-hao, XU Tai-hua, CHEN Jian-jun, SONG Jing-jing, YANG Xi-bei. Strongly Connected Components Mining Algorithm Based on k-step Search of Vertex Granule and Rough Set Theory [J]. Computer Science, 2022, 49(8): 97-107.
[2] XU Si-yu, QIN Ke-yun. Topological Properties of Fuzzy Rough Sets Based on Residuated Lattices [J]. Computer Science, 2022, 49(6A): 140-143.
[3] FANG Lian-hua, LIN Yu-mei, WU Wei-zhi. Optimal Scale Selection in Random Multi-scale Ordered Decision Systems [J]. Computer Science, 2022, 49(6): 172-179.
[4] CHEN Yu-si, AI Zhi-hua, ZHANG Qing-hua. Efficient Neighborhood Covering Model Based on Triangle Inequality Checkand Local Strategy [J]. Computer Science, 2022, 49(5): 152-158.
[5] SUN Lin, HUANG Miao-miao, XU Jiu-cheng. Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [J]. Computer Science, 2022, 49(4): 152-160.
[6] WANG Zi-yin, LI Lei-jun, MI Ju-sheng, LI Mei-zheng, XIE Bin. Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost [J]. Computer Science, 2022, 49(4): 161-167.
[7] WANG Zhi-cheng, GAO Can, XING Jin-ming. Three-way Approximate Reduction Based on Positive Region [J]. Computer Science, 2022, 49(4): 168-173.
[8] XUE Zhan-ao, HOU Hao-dong, SUN Bing-xin, YAO Shou-qian. Label-based Approach for Dynamic Updating Approximations in Incomplete Fuzzy Probabilistic Rough Sets over Two Universes [J]. Computer Science, 2022, 49(3): 255-262.
[9] LI Yan, FAN Bin, GUO Jie, LIN Zi-yuan, ZHAO Zhao. Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets [J]. Computer Science, 2021, 48(6A): 342-348.
[10] DAI Zong-ming, HU Kai, XIE Jie, GUO Ya. Ensemble Learning Algorithm Based on Intuitionistic Fuzzy Sets [J]. Computer Science, 2021, 48(6A): 270-274.
[11] XUE Zhan-ao, SUN Bing-xin, HOU Hao-dong, JING Meng-meng. Optimal Granulation Selection Method Based on Multi-granulation Rough Intuitionistic Hesitant Fuzzy Sets [J]. Computer Science, 2021, 48(10): 98-106.
[12] XUE Zhan-ao, ZHANG Min, ZHAO Li-ping, LI Yong-xiang. Variable Three-way Decision Model of Multi-granulation Decision Rough Sets Under Set-pair Dominance Relation [J]. Computer Science, 2021, 48(1): 157-166.
[13] SANG Bin-bin, YANG Liu-zhong, CHEN Hong-mei, WANG Sheng-wu. Incremental Attribute Reduction Algorithm in Dominance-based Rough Set [J]. Computer Science, 2020, 47(8): 137-143.
[14] CHEN Yu-jin, XU Ji-hui, SHI Jia-hui, LIU Yu. Three-way Decision Models Based on Intuitionistic Hesitant Fuzzy Sets and Its Applications [J]. Computer Science, 2020, 47(8): 144-150.
[15] ZHOU Jun-li, GUAN Yan-yong, XU Fa-sheng and WANG Hong-kai. Core in Covering Approximation Space and Its Properties [J]. Computer Science, 2020, 47(6A): 526-529.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!