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Soft Computing
Published in: Soft Computing 10/2019

26-04-2018 | Foundations

The comparative study of covering rough sets and multi-granulation rough sets

Authors: Qingzhao Kong, Weihua Xu

Published in: Soft Computing | Issue 10/2019

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Abstract

The covering rough set (CRS) theory and the multi-granulation rough set (MGRS) theory are both the important generalizations of Pawlak rough set theory. Up to now, substantial contributions have been made to the development of CRS and MGRS. In this paper, in order to shed some light on the comparison and combination of CRS theory and MGRS theory, we investigate the relationship between CRS and MGRS based on different aspects. We firstly put forward an effective approach to describe the covering rough sets by means of the multi-granulation rough sets. Then, we, respectively, study the differences and relations of lower and upper operators, reduction, operation properties and algebraic properties between CRS and MGRS.
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Literature
Ananthanarayana VS, Narasimha MM, Subramanian DK (2003) Tree structure for efficient data mining using rough sets. Pattern Recognit Lett 24:851–862CrossRefMATH
Comer S (1991) An algebraic approach to the approximation of information. Fundam Inform 14:492–502MathSciNetMATH
Grzy mala-Busse I, Siddhaye S (2004) Rough sets approach to rule induction from incomplete data In: Proceedings of 10th international conference on information proceeding and management of uncertainty in knowledge-based systems, pp 923–930
Jeon G, Kim D, Jeong J (2006) Rough sets attributes reduction based expert system in interlaced video sequences. IEEE Trans Consum Electron 52:1348–1355CrossRef
Kong QZ, Wei ZX (2017) Further study of multi-granulation fuzzy rough sets. J Intell Fuzzy Syst 32:2413–2424CrossRefMATH
Li DY (2002) Algebraic aspects and knowledge reduction in rough set theory. Xi\(^{^{\prime }}\)an Jiaotong University Doctor Paper
Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2016) A comparative study of multi-granulation rough sets and concept lattices via rule acquisition. Knowl-Based Syst 91:152–164CrossRef
Li JH, Huang CC, Qi JJ, Qian YH, Liu WQ (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263CrossRef
Lin GP, Liang JY, Qian YH (2013) Multigranulation rough sers: From partition to covering. Inf Sci 241:101–118CrossRefMATH
Liu CH, Wang MZ (2011) Covering fuzzy rough set based on multi-granulation. In: International conference on uncertainty reasoning and knowledge engineering, pp 146–149
Pagliani P (1998) Rough set theory and logic-algebraic structures. In: Orlowska E (ed) Studies in fuzziness and soft computing. Physica, New York, pp 109–190
Pawlak Z (1991) Rough set: theoretical aspects of reasoning about data. Kluwer Academic Publishers, DordrechtCrossRefMATH
Pomykala JA (1987) Approximation operations in approximation space. Bull Pol Acad Sci 9–10:653–662MathSciNetMATH
Pomykala JA (1988) On definability in the nondeterministic information system. Bull Pol Acad Sci Math 36:193–210MathSciNetMATH
Qian YH, Liang JY, Dang CY (2010a) Incomplete multigranulation rough set. IEEE Trans Syst Man Cybern A 20:420–431CrossRef
She YH, He XL (2012) On the structure of the multi-granulation rough set model. Knowl-Based Syst 36:81–92CrossRef
Swiniarski RW, Skowron A (2003) Rough set method in feature selection and recognition. Patt Recog Lett 24:833–849CrossRefMATH
Xu WH, Wang QR, Zhang XT (2011) Multi-granulation fuzzy rough sets in a fuzzy tolerance approximation space. Int J Fuzzy Syst 13:246–259MathSciNet
Xu WH, Li Y, Liao XW (2012) Approaches to attribute reductions based on rough set and matrix computation in inconsistent ordered information systems. Knowl-Based Syst 41(5):78–91CrossRef
Xu WH, Wang QR, Luo SQ (2014) Multi-granulation fuzzy rough sets. J Intell Fuzzy Syst 26:1323–1340MathSciNetMATH
Yang XB, Song XN, Dou HL, Yang JY (2011) Multigranulation rough set: from Crisp to Fuzzy Case. Ann Fuzzy Math Inf 1:55–70MATH
Yao YY (2001) Information granulation and rough set approximation. Int J Intell Syst 16(1):87–104CrossRefMATH
Zhang XW, Kong QZ (2016) On four types of multi-granulation covering rough sets. Fundam Inform 147:457–476CrossRefMATH
Zhu W, Wang FY (2007) On three types of covering rough sets. IEEE Trans Knowl Data Eng 19(8):1131–1144CrossRef
Metadata
Title
The comparative study of covering rough sets and multi-granulation rough sets
Authors
Qingzhao Kong
Weihua Xu
Publication date
26-04-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 10/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3205-y

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