Skip to main content
SpringerLink
Log in
Book cover

Autonomous Systems: Developments and Trends pp 135–146Cite as

Rough Sets Based Incremental Rule Acquisition in Set-Valued Information Systems

  • Chapter

  • 977 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 391))

Abstract

Set-valued information systems may evolve over time. How to acquire rules from updating information systems is vital in decision making. Rule acquisition from set-valued decision information systems needs both accuracy and coverage. To fast compute and update the accuracy and coverage, the tolerance matrix (relation matrix) and decision matrix are given when the object set varies with time. A case study on the incremental approach validates the feasibility of the proposed algorithm.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Guan, Y., Wang, H.: Set-valued information systems. Information Sciences 176(17), 2507–2525 (2006)

    Article  MathSciNet  Google Scholar 

  2. Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Information Sciences 178(18), 3577–3594 (2008)

    Article  MathSciNet  Google Scholar 

  3. Kolman, B., Busby, R.C., Ross, S.C.: Discrete Mathematical Structures, 5th edn. Prentice-Hall, Inc., Upper Saddle River (2003)

    Google Scholar 

  4. Li, T., Ruan, D., Wets, G., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowledge-Based Systems 20(5), 485–494 (2007)

    Article  Google Scholar 

  5. Liu, D., Li, T., Ruan, D., Zhang, J.: Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. Journal of Global Optimization 8, 1–20 (2010), doi:10.1007/s10898-010-9607-8

    Google Scholar 

  6. Liu, D., Li, T., Ruan, D., Zou, W.: An incremental approach for inducing knowledge from dynamic information systems. Fundamenta Informaticae 94(2), 245–260 (2009)

    MathSciNet  MATH  Google Scholar 

  7. Liu, G.: The axiomatization of the rough set upper approximation operations. Fundamenta Informaticae 69(3), 331–342 (2006)

    MathSciNet  MATH  Google Scholar 

  8. Liu, G.: Axiomatic systems for rough sets and fuzzy rough sets. International Journal of Approximate Reasoning 48(3), 857–867 (2008)

    Article  MathSciNet  Google Scholar 

  9. Michalski, R.: Knowledge repair mechanisms: evolution vs. revolution. In: Proc. ICML 1985, pp. 116–119 (1985)

    Google Scholar 

  10. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)

    Book  Google Scholar 

  11. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Sciences 177(1), 41–73 (2007)

    Article  MathSciNet  Google Scholar 

  12. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177(1), 28–40 (2007)

    Article  MathSciNet  Google Scholar 

  13. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)

    Article  MathSciNet  Google Scholar 

  14. Qian, J., Miao, D., Zhang, Z., Li, W.: Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation. International Journal of Approximate Reasoning 52(2), 212–230 (2011)

    Article  MathSciNet  Google Scholar 

  15. Qian, Y., Dang, C., Liang, J., Tang, D.: Set-valued ordered information systems. Information Sciences 179(16), 2809–2832 (2009)

    Article  MathSciNet  Google Scholar 

  16. Shan, N., Ziarko, W.: Data-based acquisition and incremental modification of classification rules. Computational Intelligence 11(2), 357–370 (1995)

    Article  Google Scholar 

  17. Zhang, J., Li, T., Ruan, D., Liu, D.: Neighborhood rough sets for dynamic data mining. In: World Conference on Soft Computing, San Francisco, May 23-26 (2011)

    Google Scholar 

  18. Zhang, W., Ma, J., Fan, S.: Variable threshold concept lattices. Information Sciences 177(22), 4883–4892 (2007)

    Article  MathSciNet  Google Scholar 

  19. Zhang, W., Mi, J.: Incomplete information system andits optimal selections. Computers & Mathematics with Applications 48(5-6), 691–698 (2004)

    Article  MathSciNet  Google Scholar 

  20. Zheng, Z., Wang, G.: RRIA: A rough set and rule tree based incremental knowledge acquisition algorithm. Fundamenta Informaticae 59(2-3), 299–313 (2004)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Junbo Zhang & Tianrui Li

  2. Belgian Nuclear Research Centre (SCK∙CEN), Boeretang 200, 2400, Mol, Belgium

    Da Ruan

  3. Department of Applied Mathematics & Computer Science, Ghent University, 9000, Gent, Belgium

    Da Ruan

Authors
  1. Junbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianrui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Da Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junbo Zhang .

Editor information

Editors and Affiliations

  1. Lehrgebiet Informationstechnik, FernUniversität Hagen, Universitätsstraße 27, Hagen, 58084, Germany

    Herwig Unger

  2. , Institut für Intelligente Systemtechnolo, Alpen Adria Universität Klagenfurt, Universitätsstraße 65-67, Klagenfurt, 9020, Austria

    Kyandoghere Kyamaky

  3. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, Warsaw, 01--447, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Zhang, J., Li, T., Ruan, D. (2012). Rough Sets Based Incremental Rule Acquisition in Set-Valued Information Systems. In: Unger, H., Kyamaky, K., Kacprzyk, J. (eds) Autonomous Systems: Developments and Trends. Studies in Computational Intelligence, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24806-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24806-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24805-4

  • Online ISBN: 978-3-642-24806-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation