PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Rough Set Approximations in Multi-granulation Fuzzy Approximation Spaces

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Pawlak’s rough set model considers the rough approximations based on an equivalence relation. Multi-granulation rough setmodels concern rough approximations based onmultiple equivalence relations. In this paper, we examine six types of rough set approximations in multi-granulation fuzzy approximation spaces (MGFASs). We construct a partition of the given universe based on a fuzzy binary relation in a fuzzy approximation space. Based on the partition, we introduce a pair of rough set approximations. In a multi-granulation fuzzy approximation space, by a family of fuzzy binary relations, we introduce two kinds of rough set approximations in terms of the union and intersection of fuzzy relations, respectively. A pair of rough set approximations based on the family of fuzzy binary relations is also discussed. Furthermore, the optimistic and pessimistic multigranulation rough set approximations are investigated due to the fuzzy binary relations in aMGFAS. Properties of these rough set approximations are demonstrated. Finally, we examine relationships of them. It is proved that the lower and upper approximations generated by a family of fuzzy binary relations are the pair nearest to the undefinable set, and the pessimistic multi-granulation lower and upper approximations are the pair farthest to the undefinable set.
Wydawca
Rocznik
Strony
145--160
Opis fizyczny
Bibliogr. 45 poz.
Twórcy
autor
  • Department of Mathematics and Information Science, Faculty of Science Chang’an University Xi’an, Shaan’xi 710064, P. R. China
autor
  • Department of Computer Science University of Regina Regina, Canada
Bibliografia
  • [1] Cai M.J., Li Q.G.: Compression of dynamic fuzzy relation information systems, Fundamenta Informaticae, manuscript
  • [2] Dubios, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems, 17, 191-208, 1990.
  • [3] Fan, T.F., Liau, C. J., Liu, D. R.: Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables, International Journal of Approximate Reasoning, 52(9), 1283-1297, 2011.
  • [4] Gediga, G., D¨untsch, I.: Modal-style operators in qualitative data analysis, in: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 155-162, 2002.
  • [5] Herbert, J. P., Yao, J. T.: Game-theoretic rough sets, Fundamenta Informaticae, 108(3-4), 267-286, 2011.
  • [6] Herbert, J. P., Yao, J. T.: Analysis of data-driven paramenters in game-theoretic rough sets, Proceedings of International Conference on Rough Sets and Knowledge Technology, Banff, Canada, LNCS 6954, 447-456, 2011.
  • [7] Hobbs, J.R.: Granularity, In: Proceeding of Internation Joint Conference on Artificial Intelligence, pp.432-435, 1985.
  • [8] Järvinen, J.: Pawlak’s information systems in terms of Galois connections and functional dependencies, Fundamenta Informaticae, 75, 315-330, 2007.
  • [9] Kryszkiewicz,M.: Rough set approach to incomplete information systems, Information Sciences, 112, 39-49, 1998.
  • [10] Kryszkiewicz, M.: Rules in incomplete information systems, Information Sciences, 113, 271-292, 1999.
  • [11] Leung, Y., Li, D.Y.: Maximal consistent block technique for rule acquisition in incomplete information systems, Information Sciences, 153, 85-106, 2003.
  • [12] Liang, J.Y., Wang, F., Dang, C.Y., Qian, Y.H.: An efficient rough feature selection algorithm with a multigranulation view, International Journal of Approximate Reasoning, 53, 912-926, 2012.
  • [13] Lin, G.P., Qian, Y.H., Li, J.J.: NMGRS: neighborhood-based multigranulation rough sets, International Journal of Approximate Reasoning, 53(7), 1080-1093, 2012.
  • [14] Lin, G.P., Liang, J.Y., Qian, Y.H.: Multigranulation rough sets: from partition to covering, Information Sciences, 241, 101-118, 2013.
  • [15] Liu, C.H., Wang, M.Z.: Covering fuzzy rough set based on multi-granulations, International Conference on Uncertainty Reasoning and Knowledge Engineering, pp. 146-149, 2011.
  • [16] Liu, C.H., Miao, D.Q., Qian, J.: On multi-granulation covering rough sets, International Journal of Approximate Reasoning, 55(6), 1404-1418, 2014.
  • [17] Ma, J.M., Zhang,W.X., Leung, Y., Song, X.X.: Granular computing and dual Galois connection, Information Sciences, 177, 5365-5377, 2007.
  • [18] Marek, V.W.: Characterizing Pawlak’s approximation operators, LNCS Transactions on Rough Sets, VI, LNCS, vol. 4400, 140-150, 2007.
  • [19] Pawlak, Z.: Rough sets, International Journal of Computer and Information Sciences, 11, 341-356, 1982.
  • [20] Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
  • [21] Pawlak, Z. and Skowron, A.: Rough sets: some extensions, Information Sciences, 177, 28-40, 2007.
  • [22] Pomykała, J.M.: Approximation operators in approximation space, Bulletin of the Polish Academy of Science, Mathematics, 35, 653-662, 1987.
  • [23] Qian, Y.H., Liang, J.Y.: Rough Set Method Based on Multi-granulations, The 5th IEEE International Conference on Cognitive Informatics, Beijing, China , 297-304, 2006.
  • [24] Qian, Y.H., Liang, J.Y., Yao, Y.Y., et al.: MGRS: Amulti-granulation rough set, Information Sciences, 180(6), 949-970, 2010.
  • [25] Qian, Y.H., Liang, J.Y., Dang, C.Y.: Incomplete multigranulation rough set, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(2), 420-431, 2010.
  • [26] Qian, Y.H., Zhang, H., Sang, Y.L., Liang, J.Y., Multigranulation decision-theoretic rough sets, International Journal of Approximate Reasoning, 55, 225-237, 2014.
  • [27] She, Y.H., He, X.L.: On the structure of the multigranulation rough set model, Knowledge-Based systems, 36, 81-92, 2012.
  • [28] Skowron, A.: Tolerance approximation spaces, Fundamenta Informaticae, 27(2-3), 245-253, 2011.
  • [29] Ślezak, D. , Ziarko,W. : The investigation of the Bayesian rough set model, International Journal of Approximate Reasoning, 40, 81-91, 2005.
  • [30] Slowinski, R., Stefanowski, J.: Rough-set reasoning about uncertain data, Fundamenta Informaticae, 27, 229-243, 1996.
  • [31] Wu, W.Z., Leung, Y., Zhang, W.X.: Connections between rough set theory and Dempster-Shafer theory of evidence, International Journal of General Systems, 31, 405-430, 2002.
  • [32] Wu, W.Z., Mi, J.S., Zhang,W.X.: Generalized fuzzy rough sets, Information Sciences, 151, 263-282, 2003.
  • [33] Wu, W.Z., Leung, Y.: Theory and applications of granular labelled partitions in multi-scale decision tables, Information Scicens, 181, 3878-3897, 2011.
  • [34] Xu, W.H., Wang, Q.R., Zhang, X.T.: Multi-granulation fuzzy rough sets in a fuzzy tolerance approximation space, International Journal of Fuzzy Systems, 13(4), 246-259, 2011.
  • [35] 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, 41(5), 475-501, 2012.
  • [36] Yang, X.B., Qian, Y.H., Yang, J.Y.: Hierarchical structures on multigranulation spaces, Journal of Computer Science and Technology, 27(6), 1169-1183, 2012.
  • [37] Yao, Y.Y., Lin, T.Y.: Generalization of rough sets using modal logic, Intelligent Automation and Soft Computing, 2(2), 103-120, 1996.
  • [38] Yao, Y.Y., Lin, T.Y.: Graded rough set approximations based on nested neighborhood systems, in: Proceedings of 5th European Congress on Intelligent Techniques and Soft Computing, 1, 196-200, 1997.
  • [39] Yao, Y.Y.: Relational interpretations of neighborhood operators and rough set approximation operators, Information Sciences, 101, 239-259, 1998.
  • [40] Yao, Y.Y.: Information granulation and rough set approximation, International Journal of Intelligent Systems, 16, 87-104, 2001.
  • [41] Yao, Y.Y., Yao, B.X.: Covering based rough set approximations, Information Sciences, 200, 91-107, 2012.
  • [42] Yeung, D.S., Chen, D.G., Tsang, E.C.C., Lee, J., Wang, X.Z.: On the genrealization on fuzzy rough sets, IEEE Transactions on Fuzzy Systems, 13, 343-361, 2005.
  • [43] Zadeh, L.A.: Fuzzy sets and information granularity, in: Advances in Fuzzy Set Theory and Application, M. Gupta, R. Ragade and R. Yager (eds.) 6, 3-18, 1979.
  • [44] Ziarko,W., Variable precision rough set model, Journal of Computer and System Science, 46(1), 39-59, 1993.
  • [45] Zhu,W.: Topological approaches to covering rough sets, Information Sciences, 177, 1499-1508, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eabb807b-7c67-4ec1-bc84-3af9d2e28751
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.