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Rough Approximations Based on Valued Tolerance Relations

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Języki publikacji
EN
Abstrakty
EN
Rough set approach for knowledge discovery in incomplete information systems has been extensively studied. This paper conduct a further study of valued tolerance relation based rough approximations. We make an analysis of the existing rough approximabilities and propose a new approach for lower (upper) approximability, which is a generalization of Pawlak approximation operators for complete information system. The approach has also been generalized to fuzzy cases. Some basic properties of the approximation operators are examined.
Wydawca
Rocznik
Strony
183--194
Opis fizyczny
Bibliogr. 21 poz., tab.
Twórcy
autor
  • College of Mathematics Southwest Jiaotong University Chengdu, Sichuan 610031, China
autor
  • College of Mathematics Southwest Jiaotong University Chengdu, Sichuan 610031, China
autor
  • Center for Radio Administration and Technology Development Xihua University Chengdu, Sichuan 610039, China
Bibliografia
  • [1] Bonikowski, Z., Bryniarski, E.,Wybraniec, U.: Extensions and intentions in the rough set theory, Information Sciences, 107, 1998, 149-167.
  • [2] Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems, 17(2-3), 1990, 191-209.
  • [3] Esteva, F., Godo, L.: Monoidal t-norm based logic: towards a logic for left-continuous t-norms, Fuzzy Sets and Systems, 124, 2001, 271-288.
  • [4] Grzymala-Busse, J.W.: On the unknown attribute values in learning from examples, Proc. of Int. Symp. On Methodologies for Intelligent Systems, 1991, 368-377.
  • [5] Grzymala-Busse, J.W., Grzymala-Busse, W., Goodwin, L.K.: A closest fit approach to missing attribute values, Proc. 7thWorkshop New Direction in Rough Sets, Data Mining, and Granular-Soft Computing Yamaguchi, Japan, Springer Verlag, LNAI 1711, 1999, 405-414.
  • [6] Grzymala-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining, Proceedings of the 2nd Int. Conference on Rough Sets and New Trends in Computing, Banff 2000, pp.340-347.
  • [7] Grzymala-Busse, J.W.: Characteristic relations for incomplete data: A generalization of the indiscernibility relation, LNAI 3066, 2004, 244-253.
  • [8] Guan, L.H., Wang, G.Y.: Generalized approximations defined by non-equivalence relations, Information Sciences, 193, 2012, 163-179.
  • [9] Kryszkiewicz, M.: Rough set approach to incomplete information system, Information Sciences, 112, 1998, 39-49.
  • [10] Kryszkiewicz,M.: Properties of incomplete information systems in the framework of rough sets, Rough Sets in Data Mining and Knowledge Discovery, Physica-Verlag, 1998, pp.422-450.
  • [11] Pawlak, Z.: Rough sets, Int. J. Computer and Information Sci., 11, 1982, 341-356.
  • [12] Pawlak, Z., Skowron, A.: Rough sets: Some extensions, Information Sciences, 177(2007)28-40.
  • [13] Qin, K., Pei, Z., Yang, J., Xu, Y.: Approximation operators on complete completely distributive lattices, Information Sciences, 247, 2013, 123-130.
  • [14] Radzikowska, A.M., Kerre, E.E.: A comparative study of fuzzy rough sets, Fuzzy Sets and Systems, 126, 2002, 137-155.
  • [15] Slowinski, R., Stefanowski, J.: Rough classification in incomplete information systems, Math. Computing Modeling, 12(10/11), 1989, 1347-1357.
  • [16] Stefanowski, J., Tsoukias, A.: Incomplete information tables and rough classification, Computational Intelligence, 17(3), 2001, 545-566
  • [17] Wang, G.Y.: Extension of rough set under incomplete information systems, Journal of Computer Research and Development (in Chinese), 39, 2002, 1238-1243.
  • [18] Yager, R.R.: On some new classes of implication operators and their role in approximate reasoning, Information Sciences, 167, 2004, 193-216.
  • [19] Yao, Y.Y.: Relational interpretation of neighborhood operators and rough set approximation operator, Information Sciences, 111, 1998, 239-259.
  • [20] Yin, X., Jia, X., Shang, L.: A new extension model of rough sets under incomplete information, Lecture Notes in Artificial Intelligence, 4062, 2006, 141-146.
  • [21] Zhang, X.H., Zhou, B., Li, P.: A general frame for intuitionistic fuzzy rough sets, Information Sciences, 216, 2012, 34-49.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-118909e5-5e36-4019-9d46-367955fd4ff2
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