Skip to main content
SpringerLink
Log in

Rough Set Approach to Information Tables with Imprecise Decisions

  • Conference paper
Rough Sets and Current Trends in Computing (RSCTC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5306))

Included in the following conference series:

Abstract

In this paper, we treat information tables with imprecise decisions, for short, imprecise decision tables. In the imprecise decision tables, decision attribute values are specified imprecisely. Under such decision tables, lower and upper object sets for a set of decision attribute values are defined. Their properties are shown. Concepts of reducts of imprecise decision tables are studied. Discernibility matrix methods are investigated for calculations of all reducts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4, 244–264 (1988)

    Article  Google Scholar 

  2. Greco, S., Matarazzo, B., Slowinski, R.: Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129, 1–47 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  3. Inuiguchi, M.: Generalization of rough sets and rule extraction. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 96–119. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. Inuiguchi, M.: Attribute reduction in variable precision rough set model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, 461–479 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Inuiguchi, M.: Structure-based attribute reduction in variable precision rough set models. Journal of Advanced Computational Intelligence and Intelligent Informatics 10, 657–665 (2006)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  7. Lipski, W.: On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems 4(3), 262–296 (1979)

    Article  Google Scholar 

  8. Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theoretical Computer Science 29, 27–39 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  9. Pawlak, Z.: Rough sets. International Journal of Information and Computer Sciences 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  10. Skowron, A., Rauser, C.M.: The discernibility matrix and functions in information systems. In: Słowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Chapter  Google Scholar 

  11. Słowiński, R., Vanderpooten, D.: A Generalized Definition of Rough Approximations Based on Similarity. IEEE Transactions on Data and Knowledge Engineering 12(2), 331–336 (2000)

    Article  Google Scholar 

  12. Yao, Y.Y., Lin, T.Y.: Generalization of Rough Sets Using Modal Logics. Intelligent Automation and Soft Computing 2(2), 103–120 (1996)

    Article  Google Scholar 

  13. Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46(1), 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531, Japan

    Masahiro Inuiguchi

  2. College of Information and Management Science, Henan Agricultural University, Zhengzhou, 450002, China

    Bingjun Li

Authors
  1. Masahiro Inuiguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bingjun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Akron, OH 44325-4003, Akron, USA

    Chien-Chung Chan

  2. Department of Electrical Engineering and Computer Science, University of Kansas, KS 66045, Lawrence, USA

    Jerzy W. Grzymala-Busse

  3. Department of Computer Science, University of Regina,, S4S 0A2, Regina, Saskatchewan, Canada

    Wojciech P. Ziarko

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Inuiguchi, M., Li, B. (2008). Rough Set Approach to Information Tables with Imprecise Decisions. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88425-5_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88423-1

  • Online ISBN: 978-3-540-88425-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation