Skip to main content

Sequential Optimization of \(\gamma \)-Decision Rules Relative to Length, Coverage and Number of Misclassifications

  • Chapter
  • First Online:
  • 465 Accesses

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8988))

Abstract

The paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to length, coverage and number of misclassifications. Presented algorithm constructs a directed acyclic graph \({\varDelta }_{\gamma }(T)\) which nodes are subtables of the decision table T. Based on the graph \({\varDelta }_{\gamma }(T)\) we can describe all irredundant \(\gamma \)-decision rules with the minimum length, after that among these rules describe all rules with the maximum coverage, and among such rules describe all rules with the minimum number of misclassifications. We can also change the set of cost functions and order of optimization. Sequential optimization can be considered as a tool that helps to construct simpler rules for understanding and interpreting by experts.

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

Buying options

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 EPUB and 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

Learn about institutional subscriptions

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: Ficarra, F.V.C. (ed.) Computational Informatics, Social Factors and New Information technologies: Hypermedia Perspectives and Avant-Garde Experienes in the Era of Communicability Expansion, pp. 29–39. Blue Herons, Bergamo (2011)

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  3. Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Optimization of approximate decision rules relative to number of misclassifications. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 674–683. IOS Press, Amsterdam (2012)

    Google Scholar 

  4. Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach for exact decision rule optimization. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. ISRL, vol. 42, pp. 211–228. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  5. 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  MATH  Google Scholar 

  6. An, A., Cercone, N.: ELEM2: a learning system for more accurate classifications. In: Mercer, R.E. (ed.) Canadian AI 1998. LNCS, vol. 1418. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  7. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007)

    Google Scholar 

  8. Bazan, J.G., Nguyen, H.S., Nguyen, T.T., Skowron, A., Stepaniuk, J.: Synthesis of decision rules for object classification. In: Orłowska, E. (ed.) Incomplete Information: Rough Set Analysis, pp. 23–57. Physica-Verlag, Heidelberg (1998)

    Chapter  Google Scholar 

  9. Błaszczyński, J., Słowiński, R., Susmaga, R.: Rule-based estimation of attribute relevance. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 36–44. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  11. Clark, P., Niblett, T.: The CN2 induction algorithm. Mach. Learn. 3(4), 261–283 (1989)

    Google Scholar 

  12. 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 

  13. Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 144–151. Morgan Kaufmann Publishers Inc., Burlington (1998)

    Google Scholar 

  14. Fürnkranz, J., Flach, P.A.: ROC ‘n’ rule learning-towards a better understanding of covering algorithms. Mach. Learn. 58(1), 39–77 (2005)

    Article  MATH  Google Scholar 

  15. Grzymala-Busse, J.W.: LERS-a system for learning from examples based on rough sets. In: Slowański, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 3–18. Springer, The Netherlands (1992)

    Google Scholar 

  16. Michalski, R.S.: A theory and methodology of inductive learning. Artif. Intell. 20(2), 111–161 (1983)

    Article  MathSciNet  Google Scholar 

  17. Michalski, S., Pietrzykowski, J.: iAQ: A program that discovers rules. AAAI-07 AI Video Competition (2007)

    Google Scholar 

  18. Moshkov, M., Chikalov, I.: On algorithm for constructing of decision trees with minimal depth. Fundam. Inform. 41(3), 295–299 (2000)

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  21. 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 

  22. Nguyen, Hung Son, Ślȩzak, Dominik: Approximate Reducts and Association Rules. In: Zhong, Ning, Skowron, Andrzej, Ohsuga, Setsuo (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 137–145. Springer, Heidelberg (1999)

    Google Scholar 

  23. Pawlak, Z.: Rough set elements. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, pp. 10–30. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  25. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  26. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  27. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  28. Sikora, M.: Decision rule-based data models using TRS and NetTRS – methods and algorithms. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XI. LNCS, vol. 5946, pp. 130–160. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  29. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowański, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory., pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  30. Ślȩzak, D., Wróblewski, J.: Order based genetic algorithms for the search of approximate entropy reducts. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS, vol. 2639, pp. 308–311. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  31. Zielosko, B.: Sequential optimization of \(\gamma \)-decision rules. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M., eds.: FedCSIS, pp. 339–346 (2012)

    Google Scholar 

  32. Zielosko, B., Moshkov, M., Chikalov, I.: Optimization of decision rules based on methods of dynamic programming. Vestnik of Lobachevsky State University of Nizhny Novgorod 6, 195–200 (2010). (in Russian)

    Google Scholar 

Download references

Acknowledgment

The author would like to thank you Prof. Mikhail Moshkov, Dr. Igor Chikalov and Talha Amin for possibility to use Dagger software system.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Beata Zielosko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Zielosko, B. (2015). Sequential Optimization of \(\gamma \)-Decision Rules Relative to Length, Coverage and Number of Misclassifications. In: Peters, J., Skowron, A., Ślȩzak, D., Nguyen, H., Bazan, J. (eds) Transactions on Rough Sets XIX. Lecture Notes in Computer Science(), vol 8988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47815-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-47815-8_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47814-1

  • Online ISBN: 978-3-662-47815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics