Skip to main content
SpringerLink
Log in

Optimization of Approximate Decision Rules Relative to Coverage

  • Conference paper
Beyond Databases, Architectures, and Structures (BDAS 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 424))

Abstract

We present a modification of the dynamic programming algorithm. The aims of the paper are: (i) study of the coverage of decision rules, and (ii) study of the size of a directed acyclic graph (the number of nodes and edges) for a proposed algorithm. The paper contains experimental results with decision tables from UCI Machine Learning Repository and comparison with results for the dynamic programming algorithm.

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. Alkhalid, A., Amin, T., Chikalov, I., Hussain, S., Moshkov, M., Zielosko, B.: Dagger: A tool for analysis and optimization of decision trees and rules. In: Computational Informatics, Social Factors and New Information Technologies: Hypermedia Perspectives and Avant-Garde Experiences in the Era of Communicability Expansion, pp. 29–39. Blue Herons (2011)

    Google Scholar 

  2. Alsolami, F., Chikalov, I., Moshkov, M., Zielosko, B.: Optimization of inhibitory decision rules relative to length. Studia Informatica 33(2A(105)), 395–406 (2012)

    Google Scholar 

  3. Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach for partial decision rule optimization. Fundam. Inform. 119(3-4), 233–248 (2012)

    MATH  MathSciNet  Google Scholar 

  4. Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach to optimization of approximate decision rules. Inf. Sci. 221, 403–418 (2013)

    Article  MathSciNet  Google Scholar 

  5. An, A., Cercone, N.: Rule quality measures improve the accuracy of rule induction: An experimental approach. In: Raś, Z.W., Ohsuga, S. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 119–129. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  6. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007), http://www.ics.uci.edu/~mlearn/

  7. Błaszczyński, J., Słowiński, R., Szeląg, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf. Sci. 181(5), 987–1002 (2011)

    Article  Google Scholar 

  8. Dembczyński, K., Kotłowski, W., Słowiński, R.: Ender: a statistical framework for boosting decision rules. Data Min. Knowl. Discov. 21(1), 52–90 (2010)

    Article  MathSciNet  Google Scholar 

  9. Moshkov, M., Piliszczuk, M., Zielosko, B.: Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications. SCI, vol. 145. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  10. Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach. SCI, vol. 360. Springer, Heidelberg (2011)

    Book  MATH  Google Scholar 

  11. Nguyen, H.S.: Approximate boolean reasoning: Foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 334–506. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Inf. Sci. 177(1), 41–73 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  13. Sikora, M., Wróbel, Ł.: Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms. Int. J. General Systems 42(6), 594–613 (2013)

    Article  MATH  Google Scholar 

  14. Zielosko, B.: Coverage of decision rules. In: Decision Support Systems, pp. 183–192, University of Silesia (2013)

    Google Scholar 

  15. Zielosko, B.: Coverage of exact decision rules. Studia Informatica 34(2A(111)), 251–262 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, 39, Bȩdzińska St., 41-200, Sosnowiec, Poland

    Beata Zielosko

Authors
  1. Beata Zielosko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Beata Zielosko .

Editor information

Editors and Affiliations

  1. Silesian University of Technology Inst. Computing Science, Gliwice, Poland

    Stanislaw Kozielski

  2. Silesian University of Technology Institute of Informatics, Gliwice, Poland

    Dariusz Mrozek

  3. Silesian University of Technology, Gliwice, Poland

    Pawel Kasprowski

  4. Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Silesian University of Technology, Gliwice,, Poland

    Daniel Kostrzewa

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zielosko, B. (2014). Optimization of Approximate Decision Rules Relative to Coverage. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures, and Structures. BDAS 2014. Communications in Computer and Information Science, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-06932-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06932-6_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06931-9

  • Online ISBN: 978-3-319-06932-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation