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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.
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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