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Rough Sets Defined by Multiple Relations

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Rough Sets (IJCRS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11499))

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Abstract

We generalize the standard rough set pair induced by an equivalence E on U in such a way that the upper approximation defined by E is replaced by the upper approximations determined by tolerances \(T_{1},\ldots ,T_{n}\) on U. Using this kind of multiple upper approximations we can express “softer” uncertainties of different kinds. We can order the set \( RS (E,T_{1},\ldots ,T_{n})\) of the multiple approximations of all subsets of the universe U by the coordinatewise inclusion. We show that whenever the tolerances \(T_{1},\ldots ,T_{n}\) are E-compatible, this ordered set forms a complete lattice. As a special case we show how this complete lattice can be reduced to the complete lattice of the traditional rough sets defined by the equivalence E.

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References

  1. Comer, S.D.: On connections between information systems, rough sets, and algebraic logic. In: Algebraic Methods in Logic and Computer Science, pp. 117–124. No. 28 in Banach Center Publications (1993)

    Article  MathSciNet  Google Scholar 

  2. Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Young Lin, T., Ohsuga, S., Liau, C.J., Hu, X. (eds.) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, vol. 9, pp. 197–212. Springer, Heidelberg (2006). https://doi.org/10.1007/11539827_11

    Chapter  Google Scholar 

  3. Järvinen, J.: Knowledge representation and rough sets. Ph.D. dissertation, Department of Mathematics, University of Turku, Finland (1999). TUCS Dissertations 14

    Google Scholar 

  4. Järvinen, J., Kovács, L., Radeleczki, S.: Defining rough sets using tolerances compatible with an equivalence. Inf. Sci. 496, 264–283 (2019)

    Article  MathSciNet  Google Scholar 

  5. Järvinen, J., Radeleczki, S.: Rough sets determined by tolerances. Int. J. Approximate Reasoning 55, 1419–1438 (2014)

    Article  MathSciNet  Google Scholar 

  6. Järvinen, J., Radeleczki, S.: Representing regular pseudocomplemented Kleene algebras by tolerance-based rough sets. J. Aust. Math. Soc. 105, 57–78 (2018)

    Article  MathSciNet  Google Scholar 

  7. Pawlak, Z.: Information systems theoretical foundations. Inf. Syst. 6, 205–218 (1981)

    Article  Google Scholar 

  8. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  Google Scholar 

  9. Pomykała, J., Pomykała, J.A.: The Stone algebra of rough sets. Bull. Pol. Acad. Sci. Math. 36, 495–512 (1988)

    MathSciNet  MATH  Google Scholar 

  10. Qian, Y., Liang, J., Yao, Y., Dang, C.: MGRS: a multi-granulation rough set. Inf. Sci. 180, 949–970 (2010)

    Article  MathSciNet  Google Scholar 

  11. Słowiński, R., Vanderpooten, D.: Similarity relation as a basis for rough approximations. ICS Research Report 53/95, Warsaw University of Technology (1995). Also in: Wang, P.P. (ed.) Advances in Machine Intelligence & Soft-Computing, vol. IV, pp. 17–33. Duke University Press, Durham, NC (1997)

    Google Scholar 

  12. Wong, S., Ziarko, W.: Comparison of the probabilistic approximate classification and the fuzzy set model. Fuzzy Sets Syst. 21, 357–362 (1987)

    Article  MathSciNet  Google Scholar 

  13. Yao, Y.Y.: Generalized rough set models. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, pp. 286–318. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  14. Yao, Y.Y.: On generalizing rough set theory. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 44–51. Springer, Berlin, Heidelberg (2003)

    Chapter  Google Scholar 

  15. Yao, Y.: A comparative study of fuzzy sets and rough sets. Information Sciences 109, 227–242 (1998)

    Article  MathSciNet  Google Scholar 

  16. Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  Google Scholar 

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Correspondence to Jouni Järvinen , László Kovács or Sándor Radeleczki .

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Järvinen, J., Kovács, L., Radeleczki, S. (2019). Rough Sets Defined by Multiple Relations. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_4

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  • DOI: https://doi.org/10.1007/978-3-030-22815-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22814-9

  • Online ISBN: 978-3-030-22815-6

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