Abstract
The multi-covering rough sets (MCRSs) are a popular aspect of rough sets. It is easy to see that classical rough sets, covering rough sets (CRSs) and multi-granulation rough sets (MGRSs) are all the special cases of the MCRSs. Recently, the algebraic theory of these rough set models mentioned above have been researched in detail. However, the algebraic theory of MCRSs has not been studied until now. It is necessary for researchers to explore the algebraic theory of MCRSs. In this paper, we focus on the operation and algebraic theories of two types of MCRS models. First, the properties of the two types of multi-covering set approximations are discussed. Especially, the properties of multi-covering approximation operators based on the unary coverings are deeply researched. Second, the operation properties with respect to intersection and union of MCRSs are researched. Meanwhile, to compute the intersection and union of MCRSs, several algorithms are constructed. Finally, on the basis of the operation properties of MCRSs, many meaningful algebraic properties of MCRSs are deeply studied.
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Apolloni B, Bassis S, Rota J, Galliani GL, Gioia M, Ferrari L (2016) A neurofuzzy algorithm for learning from complex granules. Granul Comput 1(4):225–246
Chen D, Wang C, Hu Q (2007) A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Inf Sci 177:3500–3518
Chen J, Lin Y, Lin G (2017) Attribute reduction of civering decision systems by hypergraph model. Knowl Based Syst 118:93–104
Dai J, Huang D, Su H (2014) Uncertainty measurement for covering rough sets. J Unc Fuzz Knowl Based Syst 22(2):217–233
D’eer L, Restrepo M, Cornelis C (2016) Neighborhood operators for covering-based rough sets. Inf Sci 336:21–44
Ge X, Wang P, Yun Z (2017) The rough membership functions on four types of covering-based rough sets and their applications. Inf Sci 390:1–14
Iwiński T (1987) Algebraic approach to rough sets. Bull Pol Acad Sci (Math) 35(9–10):673–683
Kong Q, Wei Z (2017) Further study of multi-granulation fuzzy rough sets. J Intell Fuzzy Syst 32:2413–2424
Kong Q, Xu W (2018) The comparative study of covering rough sets and multi-granulation rough sets. Soft Comput. https://doi.org/10.1007/s00500-018-3205-y
Kong Q, Xu W (2018b) Operation properties and algebraic application of covering rough sets. Fundam Inf 160:385–408
Lang G, Li Q, Cai M (2015) Characteristic matrixes-based knowledge reduction in dynamic covering decision systems. Knowl Based Syst 85:1–26
Lang G, Miao D (2016) Knowledge reduction of dynamic covering decision information systems when varying covering cardinalities. Inf Sci 346:236–260
Lang G, Miao D, Cai M, Zhang Z (2017) Incremental approaches for updating reducts in dynamic covering information systems. Knowl Based Syst 134:85–104
Lang G, Cai M, Fujita H, Xiao Q (2018) Related families-based attribute reduction of dynamic covering decision information systems. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2018.05.019
Li D (2002) Algebraic aspects and knowledge reduction in rough set theory, Xi’an Jiaotong University Doctor Paper
Li J, Ren Y, Mei C (2016) A comparative study of multi-granulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164
Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granulaity. Inf Sci 378:244–263
Liang M, Mi J, Feng T (2018) Optimal granulation selection for multi-label based on multi-granulation rough sets. Granul Comput. https://doi.org/10.1007/s41066-018-0110-9
Lin G, Qian Y, Li J (2012) NMGRS: neighborhood-based multi-granulation rough sets. Int J Approx Reason 53(7):1080–1093
Lin G, Liang J, Qian Y (2013) Multigranulation rough sets: from partition to covering. Inf Sci 241:101–118
Liu C, Miao D, Qian J (2014) On multi-granulation covering rough sets. Int J Approx Reason 55:1404–1418
Pagliani P (1996) Gough sets and Nelson algebras. Fundam Inf 27:205–219
Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356
Pedrycz W, Chen SM (2011) Granular computing and intelligent systems: design with information granules of higher order and higner type. Springer, Heidelberg
Pedrycz W, Chen SM (2015a) Granular computing and decision-making: interactive and interactive approaches. Springer, Heidelberg
Pedrycz W, Chen SM (2015b) Information granularity, big data, and computational intelligence. Springer, Heidelberg
Polkowski L, Skowron A (1998a) Rough sets and current trends in computing, vol 1424. Springer, Berlin
Polkowski L, Skowron A (1998b) Rough sets in knowledge discovery 1: methodology and applications. Studies in fussiness and soft computing, vol 18. Physica C, Heidelberg (ISBN: 978-3-7908-1884-0)
Polkowski L, Skowron A (1998c) Rough sets in knowledge discovery: applications, case studies and, software systems. Physica C, Heidelberg. https://doi.org/10.1007/978-3-7908-1883-3 (ISBN: 3790811203, 9783790811209)
Pomkala J (1988) On definability in the nondeterministic information system. Bulle Pol Acad Sci Math 36:193–210
Pomy Kala J, Pomy Kala JA (1988) The stone algebra of rough sets. Bulle Pol Acad Sci Math 36(7–8):495–508
Qian Y, Liang J, Yao Y, Dang C (2005) MGRS: a multi-granulation rough set. Inf Sci 180:949–970
Qian Y, Liang J, Wei W (2010) Pessimistic rough decision. In: Second international workshop on rough sets theory, Zhoushan, P.R. China, pp 440-449
Skowron A, Stepaniuk J (1996) Tolerance approximation spaces. Fundam Inf 27:245–253
Slowinski R, Vanderpooten D (2000) A generalized definition of rough approximations based on similarity. IEEE Trans Knowl Data Eng 12:331–336
Wang L, Yang X, Wu C (2013) Multi-covering based rough set model. In: Ciucci D et al (eds) RSFDGrC 2013, LNAI 8170. Springer-Verlag, Berlin, pp 236–244
Wang C, Shao M, Sun B (2015) An improved attribute reduction scheme with covering based rough sets. Appl Soft Comput 26:235–243
Wang G, Yang J, Xu J (2017) Granular computing: from granularity optimization to multi-granularity joint problem solving. Granul Comput 2(3):105–120
Wu W, Zhang W (2006) Rough set approximations vs. measurable spaces. In: IEEE GrC, pp 329-332
Xu W, Zhang W (2007) Measuring roughness of generalized rough sets induced by a covering. Fuzzy Sets Syst 158:2443–2455
Xu W, Sun W, Zhang X, Zhang W (2012) Multile granulation rough set approach to ordered information systems. Int J Gen Syst 41(5):471–501
Xu W, Wang Q, Zhang X (2013) Multi-granulation rough sets based on tolerance relations. Soft Comput 17:1241–1252
Xu W, Guo X (2016) Generalized multigranulation double-quantitative decision-theoretic rough set. Knowl Based Syst 105:190–205
Xu W, Li W, Zhang X (2017) Generalized multigranulation rough sets and optimal granularity selection. Granul Comput. https://doi.org/10.1007/s41066-017-0042-9
Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1(1):21–35
Yang X, Song X, Chen Z, Yang J (2012) On multigranulation rough sets in incomplete information system. Int J Mach Learn Cybern 3:223–232
Yang B, Zhu W (2014) A new type of covering-based rough sets, In: 9th International conference on rough sets and knowledge technology, Shanghai, P.R.China, pp 489–499
Yao Y, Lin T (1996) Generalization of rough sets using model logic. Intell Autom Soft Comput Int J 2:103–120
Yao Y (1998) Relational interpretations of neighborhood operators and rough set approximation operators. Inf Sci 101:239–259
Yao Y (2003) On generalizing rough set theory. In: Proceeding of the ninth international conference on rough sets, fuzzy sets, data mining and granular computing LNCS(LNAI) 2639, pp 44-51
Yao Y, Chen Y (2005) Subsystem based generalizations of rough set approximations. LNCS 3488:210–218
Yao Y, Yao B (2012) Covering based rough set approximations. Inf Sci 200(1):91–107
Yao Y, She Y (2016) Rough set models in multigranulation spaces. Inf Sci 327:40–56
Zakowski W (1983) Approximations in the space (\(u,\pi\)). Demonstr Math 16:761–769
Zhang N, Yao Y, Ohshima M (2003) Pecularity oriented multidatabase mining. IEEE Trans Knowl Data Eng 15(4):952–960
Zhu W (2007) Generalized rough sets based on relations. Inf Sci 177(22):4997–5001
Zhu W, Wang F (2006) Covering based granular computing for conflict analysis. In: IEEE ISI vol 3975 of LNCS, pp 566-571
Zhu W, Wang S (2011) Matroidal approaches to generalized rough sets based on relations. Int J Mach Learn Cybern 2(4):273–279
Acknowledgements
The authors are very grateful to the reviewers and editor for their valuable suggestions. This work is partially supported by the National Natural Science Foundation of China (Nos. 61105041, 61472463, 61402064, 61772002), the National Natural Science Foundation of CQ CSTC (No. cstc2015jcyjA40053), the Natural Science Foundation of Fujian Province (Nos. 2017J01763, 2016J01735, 2016J01022, 2016J01310), the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJ1709221), the Macau Science and Technology Development Foundation (No. 081/2015/A3), the Foundation of Education Department of Fujian Province, China (No. JAT160369), and the Research Startup Foundation of Jimei University (NO. ZQ2017004).
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Kong, Q., Zhang, X. & Xu, W. Operation properties and algebraic properties of multi-covering rough sets. Granul. Comput. 4, 377–390 (2019). https://doi.org/10.1007/s41066-018-0137-y
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DOI: https://doi.org/10.1007/s41066-018-0137-y