Research on weighted and degree multi granularity intuitionistic fuzzy rough set model based on multiple parameters
Authors: 
X Y Wang, T Zhu, Y X ShenAuthors Info & Claims
X Y Wang, T Zhu, Y X ShenAuthors Info & ClaimsArticle No.: 107, Pages 1 - 5
Abstract
In view of the need to expand the existing multi granularity intuitionistic fuzzy rough set model in practical application, it is proposed that the weighted and degree multi granularity intuitionistic fuzzy rough set model, the weighted and degree multi granularity intuitionistic fuzzy rough set model based on multiple parameters, and the weighted and degree multi granularity intuitionistic fuzzy rough set model based on average granularity weight and multiple parameters respectively. The definitions of upper and lower approximation of the correlation model are given, and the correlation properties of the model are studied.
References
[1]
Zhang W X, Wu W Z, Liang J Y, Rough set theory and method[M].Beijing:Science Press, 2001:1-30.
[2]
Yang S L, Ni Z W.Machine learning and intelligent decision support system[M].Beijing:Science Press, 2004:1-50.
[3]
Pawlak Z.Rough sets[J].International Journal of Compute and Information Science, 1982, 11 (4) :341-356.
[4]
Qian Y H,Liang J Y,Yao Y Y,etal.MGRS:A multi-granulation rough set[J].Information Sciences,2010,180(6):949-970.
[5]
Yanli Sang, Yuhua Qian. A granularity reduction algorithm for pessimistic multi granularity rough sets [J]. Pattern Recognition and Artificial Intelligence, 2012, 25(03): 361-366.
[6]
Xu W H,Wang Q R.Multi-granulation fuzzy rough sets in a fuzzy tolerance approximation space[J].International Journal of Fuzzy Systems,2011,13(4):246-259.
[7]
Lin G P,Qian Y H,Li J J.NMGRS:neighborhood-based multigranulation rough sets[J].International Journal of Approximate Reasoning,2012,53(7):1080-1093.
[8]
Qian Y H,Li S Y,Liang J Y,etal.Pessimistic rough set based decisions:a multi-granulation fusion strategy[J].Information Sciences,2014,264:196-210.
[9]
Liu C H,Miao D Q,QianJ.On multi-granulation covering rough sets[J].International Journal of Approximate Reasoning,2014,55(6):1404-1418.
[10]
Rui Ma, Wenqi Liu. Multi granularity rough set based on set-valued information system [J]. Systems Engineering and Electronics,2014, 36(05): 920-925.
[11]
Yingjie Lei, Jie Zhao, Yanli Lu. Intuitionistic fuzzy set theory and its application[M].BeiJing:Science Press.2014.
[12]
AtanassovK.Intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1986,20(1):87-96.
[13]
Huang B,Wei D K,Li H X,etal.Using a rough set model to extract rules in dominance-based interval-valued intuitionistic fuzzy information systems[J].Information Sciences,2013,221(2):215-229.
[14]
Huang B,Li H X,Wei D K.Dominance-based rough set model in intuitionistic fuzzy information systems[J].KnowledgeBased Systems,2012,28(1):115-123.
[15]
Huang B, Wei D K. Intuitionistic fuzzy rough model based on distance and its application [J]. Systems Engineering-Theory & Practice,2011,31(07):1356-1362.
[16]
Qing Guo, Lei Wu, Rough sets and decision making of intuitionistic fuzzy information systems under multi granularity[J]. Systems Engineering and Electronics, 2016. 38(02): 347-351.
[17]
Qing Guo, Ming Yin, Lei Wu, Superiority relation and reduction of intuitionistic fuzzy information system [J]. Systems Engineering and Electronics, 2014. 36(11): 2239-2243.
[18]
Xia Ji, Peng Zhao, Sheng Yao, Rough set model of weighted multi granularity intuitionistic fuzzy information system and its decision making[J]. Pattern Recognition and Artificial Intelligence, 2017. 30(11): 971-982.
[19]
ZhanaoXue, Liping Zhao, Min Zhang,Haodong Hou, Multi-attribute decision making method based on multi granularity support intuitionistic fuzzy rough sets [J]. Pattern Recognition and Artificial Intelligence. 2019, 32(08): 677-690.
[20]
Weihua Xu, Kehua Yuan, Wentao Li, Multi attribute intuitionistic fuzzy tolerance rough set model [J]. Journal of Nanjing University of Information Science & Technology, 2020, 12(03): 284-288.
[21]
Lei Wu, Shanlin Yang, Qing Guo, Upper approximation reduction of intuitionistic fuzzy target information system under dominance relation[J]. Pattern Recognition and Artificial Intelligence,2014,27(04):300-304.
[22]
Zhiyuan Wu, Peihua Zhong, Jiangen Hu, Degree multi granularity rough set [J]. Fuzzy Systems and Mathematics, 2014. 28(03): 165-172.
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
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Association for Computing Machinery
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Published: 25 February 2022
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- Natural Science Fund Project of Anhui Province
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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