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Tytuł artykułu

Approximate decision logic and reduction of decision rules

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper generalises Pawlak's rough approach to Reduction of Decision Rules using Decision Logic language (DL-language) by introducing Approximate Decision Logic language (ADL-language) based on almost indiscernibility relation. An information system has been considered where attribute values are not always quantitative, rather subjective having vague or imprecise meanings. Some objects may have attribute values which are almost identical. This observation has been analysed here based on fuzzy proximity relations on different domains of attributes.
Rocznik
Strony
3--16
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
autor
  • Department of Mathematics, Indian Institute of Technology, Kharagpur - 721302, India
autor
  • Department of Mathematics, Indian Institute of Technology, Kharagpur - 721302, India
Bibliografia
  • [1] Boryczka, M., Optimization of decision tables using rough sets, Buli. Polish Acad. Sci. Tech., 37, 1989, 321-332.
  • [2] De, S.K., Biswas, R., Roy, A. R., Finding dependency of attributes in an information system, The Journal of Fuzzy Math, 7, 1999, 335-343.
  • [3] Dubois, D., and Prade, H., Rough Fuzzy Sets and Fuzzy Rough Sets, International Journal of General Systems, 17, 1990, 191 - 209.
  • [4] Greco, S., Matarazzo, B., and Słowiński, R., Fuzzy Similarity Relation as a basis for Rough Approximations. In Polkowski L., Skowron A. (eds.), Proc. of the First International Conference on Rough Sets and Current Trends in Computing, Springer Verlag, Berlin, LNAI 1424, 1998, 283 - 289.
  • [5] Greco, S., Matarazzo, B., and Słowiński, R., Rough Set Processing of Vague Information using Fuzzy Similarity Relations, In Calude C. S., Paun G. (eds.), Finite vs Infinite: contributions to an eternal dilemma, Springer Verlag, Berlin 2000, 149 - 173.
  • [6] Kowalczyk, A. and Szymański, J., Rough Simplification of Decision Tables, Buli. Polish Acad. Sci. Tech., 37, 1989, 359-374.
  • [7] Krynicki, M., A Note on Rough Concept Logic, Fundamenta Informaticae, 13, 1990, 227-235.
  • [8] Mrozek, A., Rough Sets and Dependency Analysis among Attributes in Computer Implementation of Expert Inference Models, International Journal of Man-Machine Studies, 30, 1989, 457-473.
  • [9] Orłowska, E., Logic Aspects of Learning Concepts, International Journal of Approximating Reasoning, 2, 1988, 349-364.
  • [10] Orłowska, E., Semantics Analysis of Inductive Reasoning, Theoretical Computer Science, 43, 1986, 81-89.
  • [11] Pawlak, Z., Rough Sets, International Journal of Information and Computer Sciences, 11, 1982, 341- 356.
  • [12] Pawlak, Z., Rough Logic, Bull. Polish Acad. Sci. Tech., 35, 1987, 253-258.
  • [13] Pawlak, Z., Rough Sets, Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, 1991.
  • [14] Skowron, A., and Stepaniuk, J., Tolerance Approximation Spaces, Fundamenta Informaticae, 27, 1996, 245 - 253.
  • [15] Słowiński, R., and Vanderpooten, D., Similarity Relation as a basis for Rough Approximations, in Wang, P. (eds.), , Advances in Machine Intelligence and Soft Computing vol. IV, Duke University Press, 1997, 17 - 33.
  • [16] Stefanowski, J., and Tsoukias, A., Valued Tolerance and Decision Rules, in W. Ziarko, Y. Yao (eds.), Proceedings of the RSCTC2000 Conference, Banff, 2000, 180 - 187.
  • [17] Yao, Y., Combination of Rough Sets and Fuzzy Sets based on a-level sets, In Lin T. Y., Cercone N. (eds.), Rough Sets and Data Mining, Kluwer Academic, Dordrecht, 1996, 301 - 321.
  • [18] Yao, Y., and Wang, T., On Rough Relations: an Alternative Formulation, In N. Zhong, A. Skowron, S. Ohsuga, (eds.), New Directions in Rough Sets, Data Mining and Granular-Soft Computing, Springer Verlag, LNAI1711, Berlin, 1999, 82 - 90.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0035-0075
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