Tolerance-based multigranulation rough sets in incomplete systems

Zaiyue ZHANG, Xibei YANG

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PDF(294 KB)
Front. Comput. Sci. ›› 2014, Vol. 8 ›› Issue (5) : 753-762. DOI: 10.1007/s11704-014-3141-7
RESEARCH ARTICLE

Tolerance-based multigranulation rough sets in incomplete systems

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Abstract

Presently, the notion ofmultigranulation has been brought to our attention. In this paper, the multigranulation technique is introduced into incomplete information systems. Both tolerance relations and maximal consistent blocks are used to construct multigranulation rough sets. Not only are the basic properties about these models studied, but also the relationships between different multigranulation rough sets are explored. It is shown that by using maximal consistent blocks, the greater lower approximation and the same upper approximation as from tolerance relations can be obtained. Such a result is consistent with that of a single-granulation framework.

Keywords

incomplete information system / maximal consistent block / multigranulation rough sets / tolerance relation

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Zaiyue ZHANG, Xibei YANG. Tolerance-based multigranulation rough sets in incomplete systems. Front. Comput. Sci., 2014, 8(5): 753‒762 https://doi.org/10.1007/s11704-014-3141-7

References

[1]
Pawlak Z. Rough Sets-Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, 1992
[2]
Gore A. Earth in the Balance. New York: Plume Books, 1992
[3]
Ebenbach D H, Moore C F. Incomplete information, inferences, and individual differences: the case of environmental judgments. Organizational Behavior and Human Decision Processes, 2000, 81: 1-27
CrossRef Google scholar
[4]
Yang X B, Yang J Y. Incomplete information system and rough set theory: models and attribute reductions. Science Press & Springer, 2012
[5]
Yang X B, Zhang M. Dominance-based fuzzy rough approach to an interval-valued decision system. Frontiers of Computer Science in China, 2011, 5: 195-204
CrossRef Google scholar
[6]
Alonso S, Chiclana F, Herrera F, Herrera-Viedma, Alcalá-Fdez, Porcel C. A consistency based procedure to estimate missing pairwise preference values. International Journal of Intelligent Systems, 2008, 23: 155-175
CrossRef Google scholar
[7]
Herrera-Viedma E, Chiclana F, Herrera F, . Group decision-making model with incomplete fuzzy preference relations based on additive consistency. IEEE Transactions on Systems, Man, and Cybernetics Part B, 2007, 37: 176-189
CrossRef Google scholar
[8]
Kryszkiewicz M. Rough set approach to incomplete information systems. Information Sciences, 1998, 112: 39-49
CrossRef Google scholar
[9]
Leung Y, Li D Y. Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences, 2003, 115: 85-106
CrossRef Google scholar
[10]
Leung Y, Wu W Z, Zhang W X. Knowledge acquisition in incomplete information systems: A rough set approach. European Journal of Operational Research, 2006, 168: 164-180
CrossRef Google scholar
[11]
Shao M W, Zhang W X. Dominance relation and rules in an incomplete ordered information system. International Journal of Intelligent Systems, 2005, 20: 13-27
CrossRef Google scholar
[12]
Yang X B, Yang J Y, Wu C, Yu D J. Dominance-based rough set approach and knowledge reductions in incomplete ordered information system. Information Sciences, 2008, 178: 1219-1234
CrossRef Google scholar
[13]
Stefanowski J, Tsoukias A. Incomplete information tables and rough classification. Computational Intelligence, 2001, 17: 545-566
CrossRef Google scholar
[14]
Qian Y H, Liang J Y. Rough set method based on multi-granulations. 5th IEEE International Conference on Cognitive Informatics, 2006: 297-304
[15]
Qian Y H, Liang J Y, Dang C Y. Incomplete multigranulation rough set. IEEE Transactions on Systems, Man, and Cybernetics Part B, 2010, 20: 420-431
CrossRef Google scholar
[16]
Qian Y H, Liang J Y, Pedrycz W, Dang C Y. Positive approximation: an accelerator for attribute reduction in rough set theory. Artificial Intelligence, 2010, 174: 597-618
CrossRef Google scholar
[17]
Qian Y H, Liang J Y, Wei W. Pessimistic rough decision. Second International Workshop on Rough Sets Theory, 2010: 440-449
[18]
Qian Y H, Liang J Y, Yao Y Y, Dang C Y. MGRS: a multi-granulation rough set. Information Sciences, 2010, 180: 949-970
CrossRef Google scholar
[19]
Liang J Y, Wang F, Dang C Y, Qian Y H. An efficient rough feature selection algorithm with a multi-granulation view. International Journal of Approximate Reasoning, 2012, 53: 912-926
CrossRef Google scholar
[20]
Yang X B, Zhang Y Q, Yang J Y. Local and global measurements of MGRS rules. International Journal of Computational Intelligence Systems, 2012, 5: 1010-1024
CrossRef Google scholar
[21]
Yang X B, Song X N, Chen Z H, Yang J Y. On multigranulation rough sets in incomplete information system. International Journal of Machine Learning and Cybernetics, 2012, 3: 223-232
CrossRef Google scholar
[22]
Yang X B, Qi Y S, Song X N, Yang J Y. Test cost sensitive multigranulation rough set: model and minimal cost selection. Information Sciences, 2013, 250: 184-199
CrossRef Google scholar
[23]
Yang X B, Song X N, She X H, Yang J Y. Hierarchy on multigranulation structures: a knowledge distance approach. International Journal of General Systems, 2013, 42: 754-773
CrossRef Google scholar
[24]
Xu W H, Sun W X, Zhang X Y, Zhang W X. Multiple granulation rough set approach to ordered information systems. International Journal of General Systems, 2012, 41: 475-501
CrossRef Google scholar
[25]
Xu W H, Wang Q R, Zhang X T. Multi-granulation rough sets based on tolerance relations. Soft Computing, 2013, 17: 1241-1252
CrossRef Google scholar
[26]
XuW H, Wang Q R, Zhang X T. Multi-granulation fuzzy rough sets in a fuzzy tolerance approximation space. International Journal of Fuzzy Systems, 2011, 13: 246-259
[27]
Lin G P, Qian Y H, Li J J. NMGRS: Neighborhood-based multigranulation rough sets. International Journal of Approximate Reasoning, 2012, 53: 1080-1093
CrossRef Google scholar
[28]
Lin G P, Liang J Y, Qian Y H. Multigranulation rough sets: From partition to covering. Information Sciences, 2013, 241: 101-118
CrossRef Google scholar
[29]
Guan Y Y, Wang H K. Set-valued information systems. Informa<?Pub Caret?>tion Sciences, 2006, 176: 2507-2525
CrossRef Google scholar

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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