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Fuzzy Information and Engineering pp 825–836Cite as

γ-Tolerance Relation-Based RS Model in IFOIS

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Part of the book series: Advances in Soft Computing ((AINSC,volume 40))

Abstract

The traditional rough set (RS) theory is a powerful tool to deal with complete information system, and its performance to process incomplete information system is weak, especially, its effect of combining the incomplete information system with fuzzy objective information system is weaker. The paper improves the tolerance relation proposed by M.Kryszkiewcz to obtain the γ− tolerance relation and γ− tolerance classes, presents the concept of the incomplete and fuzzy objective information system (IFOIS in short), and gives its rough set model based on the γ− tolerance relation, i.e., the rough set model in incomplete and fuzzy objective information system. Finally, the concept of precision reduction is defined, and the corresponding algorithm is provided.

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References

  1. Wang, G.Y.: Rough Set Theory and Knowledge Discovery. Xi’an JiaoTong University Press (2, Xi’an (2001)

    Google Scholar 

  2. Wang, G.Y., Wu, Y., Fisher, P.S.: Rule Generation Based on Rough Set Theory. In: Dasarathy, B.V. (ed.) Data Mining and Knowledge: Theory, Tools, and Technology II, pp. Proceedings of SPIE vol. 4057, 181–189 (2000)

    Google Scholar 

  3. Wu, W.Z., Mi, J.S., Zhang, W.X.: A New Rough Set Approach to Knowledge Discovery in Incomplete Information System. In: Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, 2-5 November 2003, pp. 1713–1718 (2003)

    Google Scholar 

  4. Liu, Q.: Rough Set and Rough Reasoning. Science Press, Beijing (2001)

    Google Scholar 

  5. Kryszkiewicz, M.: Rough set approach to incomplete information system. Information Sciences 112, 39–49 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  6. Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113, 271–292 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  7. Zhang, H.Y., Liang, J.Y.: Variable Precision Rough Set Model and a Knowledge Reduction Algorithm for Incomplete Information System. Computer science 30(4), 153–155 (2003)

    Google Scholar 

  8. He, W., et al.: An Algorithm of Attributes Reduction in Incomplete Information System. Computer science 31(2), 153–155 (2004)

    Google Scholar 

  9. Huang, B., Wei, D.K., Zhou, X.Z.: An algorithm for Maximum Distribution Reductions and Maximum Distribution Rules in Information system. Computer science 31(10A), 80–83 (2004)

    Google Scholar 

  10. Huang, B., Zhou, X.Z.: Extension of Rough Set Model Based on Connection Degree under Incomplete Information System. Systems Engineering –Theory and Practice 1, 88–92 (2004)

    Google Scholar 

  11. Slowinski, R., Vanderpooten, D.: A Generalized Definition of Rough Approximation Based on Similarity. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 12(2), 331–336 (2000)

    Article  Google Scholar 

  12. Zhang, W.X., Leung, Y., Wu, W.Z.: Information System and Knowledge Discovery. Science Press, Beijing (2003)

    Google Scholar 

  13. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International Journal of Systems 17, 191–208 (1990)

    Article  Google Scholar 

  14. Shen, Q., Chouchoulas, A.: A rough-fuzzy approach for generating classification rules. Pattern Recognition 35, 2425–2438 (2002)

    Article  MATH  Google Scholar 

  15. Sarkar, M.: Rough-fuzzy functions in classification. Fuzzy Sets and Systems 132, 353–369 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  16. Wu, W.Z., Zhang, W.X., Xu, Z.B.: Characterizating Rough Fuzzy Set in Constructive and Axiomatic Approaches. Chinese Journal of Computer 27(2), 197–203 (2004)

    MathSciNet  Google Scholar 

  17. Liu, G.L.: The Axiomatic Systems Rough Fuzzy Set on Fuzzy Approximation Space. Chinese Journal of Computer 27(9), 1187–1191 (2004)

    Google Scholar 

  18. Zhang, M., Li, H.Z., Zhang, W.X.: Rough Set Theory on Fuzzy Information System. Fuzzy Systems and Mathematics 16(3), 44–49 (2002)

    MathSciNet  Google Scholar 

  19. Wei, D.K., Huang, B., Zhou, X.Z.: Rough Set Model and Knowledge Reduction in Incomplete and Fuzzy Objective Information system. Computer Engineering 32(8), 48–51 (2006)

    Google Scholar 

  20. Wei, D.K.: Similarity Relation-Based Knowledge Reduction in Incomplete and Fuzzy Decision Information Systems. Journal of Nature Science of Hunan Normal University 29(2), 18–23 (2006)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics and computing Science, Hunan University of Science and Engineering, Yongzhou, Hunan, 425100, P.R. China

    Lujin Tang

  2. College of Information Engineering, Hunan University of Science and Engineering, Yongzhou, Hunan, 425100, P.R. China

    Dakuan Wei

Authors
  1. Lujin Tang
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  2. Dakuan Wei
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Editor information

Bing-Yuan Cao

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© 2007 Springer-Verlag Berlin Heidelberg

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Tang, L., Wei, D. (2007). γ-Tolerance Relation-Based RS Model in IFOIS. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_90

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  • DOI: https://doi.org/10.1007/978-3-540-71441-5_90

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71440-8

  • Online ISBN: 978-3-540-71441-5

  • eBook Packages: EngineeringEngineering (R0)

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