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A Practicable Heuristic Attributes Reduction Algorithm for Ordered Information Systems

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Foundations of Intelligent Systems

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 122))

Abstract

Rough set theory is a mathematic tool to handlevague and uncertain knowledge. The core problem of rough set theory is the reduction of attributes. This paperintroduces apresentation of the information granularity for ordered information systems based on dominance relation, and detail the way to calculate the attribute importance based on the information granularity we have defined. On that basis, put forward a heuristic attribute reduction algorithm for ordered information systems. Experiments show that the algorithm performs well and outperforms some other rivals.

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References

  1. Pawlak, Z.: Roughsets. International Journal of Computer and Information Science 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  2. Chmielewski, M.R., Grzymala-Busse, J.W.: Globaldiscretization of continuous attributes aspreprocessing for machine learning. International Journal of Approximate Reasoning 15, 319–331 (1996)

    Article  MATH  Google Scholar 

  3. Lingras, P.J., Yao, Y.Y.: Data mining using extensionsof the rough set model. Journal of the AmericanSociety for Information Science 49(5), 415–422 (1998)

    Article  Google Scholar 

  4. McSherry, D.: Knowledge discovery by inspection. Decision Support Systems 21, 43–47 (1997)

    Article  Google Scholar 

  5. Pomerol, J.C.: Artificial Intelligenceand HumanDecision Making. European Journal of Operational Research 99, 3–25 (1997)

    Article  MATH  Google Scholar 

  6. Slowinski, R.: Intelligent Decision Support: Handbookof Applications and Advances of the Rough Sets Theory, pp. 23–33. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  7. Greco, S., Matarazzo, B., Slowingski, R.: Rough approximation of a preference relation by dominance relation. European Journal of Operation Research 117, 63–83 (1999)

    Article  MATH  Google Scholar 

  8. Greco, S., Matarazzo, B., Słowiński, R.: A New Rough Set Approach to Multicriteria And Multiattribute Classification. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 60–67. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  9. Greco, S., Matarazzo, B., Slowiński, R.: Rough approximation by dominance relations. International Journal of Inteligent System 17(2), 153–171 (2002)

    Article  MATH  Google Scholar 

  10. Wong, S.K.M., Ziarko, W.: On Optimal Decision Rules in Decision Tables. Bulletin of Polish Academy of Sciences 33(11-12), 693–696 (1985)

    MathSciNet  MATH  Google Scholar 

  11. Zhang, W.X., Liang, Y., Wu, W.Z.: Information Systems and Knowledge Discovery. Science Press, Beijing (2003) (in Chinese)

    Google Scholar 

  12. Xu, W.H., Zhang, W.Y.: Knowledge Reductions in Inconsistent Information Systems Based on Dominance Relations. Computer Science 33(2), 182–184 (2006)

    Google Scholar 

  13. Shao, M.W., Zhang, W.X.: Dominance Relation and Rules in an Incomplete Ordered Information System. International Journal of Intelligent Systems 20, 13–27 (2005)

    Article  MATH  Google Scholar 

  14. Miao, D.Q., Wang, Y.: An Information Representation of the Concepts and Operations in Rough Set Theory. Journal of Software 10(2), 113–116 (1999)

    Google Scholar 

  15. Wang, G.Y.: Algebra View And Information View of Rough Sets Theory: Data Mining and Knowledge Discovery Theory, Tools, and Technology III. In: Proceeding of SPIE, vol. 4384, pp. 200–207 (2001)

    Google Scholar 

  16. Gui, X.C.: Attribute Reduction Algorithm Based on the Relative Entropy. Computer Engineering and Applications 42(33), 157–159 (2006)

    MathSciNet  Google Scholar 

  17. Liang, J.Y., Xu, Z.B.: The Algorithm on Knowledge Reduction in Incomplete Information Systems. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 24(1), 95–103 (2002)

    Article  MathSciNet  Google Scholar 

  18. Liang, J.Y., Li, D.Y.: Uncertainty in Information Systems and Knowledge Acquisition. Science Press, Beijing (2005) (in Chinese)

    Google Scholar 

  19. Zhang, X.Y., Xu, W.H.: Entropy of Knowledge and Rough Set in Ordered Information Systems. Computer Engineering and Applications 43(27), 62–65 (2007)

    Google Scholar 

  20. Wang, Y., Qian, Y.H., Liang, J.Y.: Heuristic Attribute Reducton Algorithm to Ordered Information Systems. Computer Science 1, 258–260 (2010)

    Google Scholar 

  21. Gu, J.H., Zhou, Y.C., Song, J., Yan, J.Q.: A New Attribute Value Reduction Algorithm. Acta Scientiarum Naturalium University Nankaiensis 36(4), 38–42 (2003) (in Chinese)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Supported by the Fundamental Research Funds for the Central Universities(1118021707) Software Institute, Nanjing University, Nanjing, Jiangsu Province, 210093, China

    Wei Li, Feng Liu & Zhi-hong Zhao

Authors
  1. Wei Li
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  2. Feng Liu
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  3. Zhi-hong Zhao
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Yinglin Wang

  2. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, China

    Tianrui Li

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© 2011 Springer-Verlag Berlin Heidelberg

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Li, W., Liu, F., Zhao, Zh. (2011). A Practicable Heuristic Attributes Reduction Algorithm for Ordered Information Systems. In: Wang, Y., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25664-6_18

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  • DOI: https://doi.org/10.1007/978-3-642-25664-6_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25663-9

  • Online ISBN: 978-3-642-25664-6

  • eBook Packages: EngineeringEngineering (R0)

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