WSEAS Transactions on Information Science and Applications


Print ISSN: 1790-0832
E-ISSN: 2224-3402

Volume 15, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.



Algorithms for Approximations in MGRSM Based on Maximal Compatible Granules

AUTHORS: Chen Wu, Dandan Li, Ronghua Yang, Lijuan Wang, Xibei Yang

Download as PDF

ABSTRACT: This paper emphasizes studying on the properties of approximations in rough set and multi-granulation rough set models based on maximal compatible classes as primitive ones in which any two objects are mutually compatible, obtains several theorem results, proposes and designs the upper and lower approximation computation algorithms in multi-granulation rough set model. It verifies the correctness of algorithms by examples and experiments

KEYWORDS: incomplete information system; rough set model; maximal compatible class; algorithm; multi-granulation

REFERENCES:

[1] Z.Pawlak, “Rough sets and intelligent data analysis”, Information Sciences. 147 (2002) ,pp.1-12

[2] W.Roman, Q.Swiniarski and A.Skowron, “Rough Set Method in Feature Selection and Recognition”, Pattern Recognition Letters, 24 (2003),pp. 833~849

[3] J.S.Mi,W.Z.Wu and W.X.Zhang, “Approaches to Knowledge Reduction Based on Variable Precision Rough Set Model”, Information Sciences. 159 (2004),pp.255-272

[4] M.Kryszkiewicz, “Rough Set Approach to Incomplete Information Systems”,Information Sciences, 112 (1998), pp.39-49

[5] J.Stefanowski, “Incomplete Information Tables and Rough Classification”,J. Computational Intelligence. 17 (2001),pp.545-566

[6] W. G. Yin, M. Y. Lu, Variable precision rough set based decision tree classifier. Journal of Intelligent and Fuzzy Systems, 23 (2012), 61-70

[7] W.L.Chen,J.X.Cheng and C.J.Zhang, “A Generalization to Rough Set Theory Based on Tolerance Relation”, J. computer engineering and applications, 16(2004),pp. 26-28

[8] C.Wu, X.B.Yang, “Information Granules in General and Complete Covering”, Proceedings of the 2005 IEEE International Conference on Granular Computing, pp. 675-678

[9] Leung,Yli D Y. “Maximal consitent block technique for rule acquisition in incomplete information systems”.Information Sciences,2003,153,pp.86-106

[10] Qian Y H, Liang J Y, Yao Y Y, C, et al, “MGRS: a multigranulation rough set”, Information Sciences, 2010,vol.180,no.6,pp.949–970

[11] Qian Y. H, Liang J. Y, Dang C Y. “Incomplete multigranulation rough set”, IEEE Transactions on Systems, Man and Cybernetics, Part A, 2010,vol.40,no.2,pp. 420-431

[12] Qian Y H, Liang J Y, Wei W. “Pessimistic rough decision”, in: Second International Workshop on Rough Sets Theory, October 2010, Zhoushan,China, pp. 440–449

[13] C.W, X.Hu, Z.Li, X.Zhou, P. Achananuparp. “Algorithms for Different Approximations in Incomplete Information Systems with Maximal Compatible Classes as Primitive Granules”. Proc. of IEEE International Conference on Granular Computing, GrC 2007, San Jose, California, USA, 2-4 November 2007. IEEE 2007 pp.169-174

WSEAS Transactions on Information Science and Applications, ISSN / E-ISSN: 1790-0832 / 2224-3402, Volume 15, 2018, Art. #1, pp. 1-6


Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Bulletin Board

Currently:

The editorial board is accepting papers.


WSEAS Main Site