Optimization of Decision Rules Relative to Coverage - Comparative Study

  • Beata Zielosko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)


In the paper, we present a modification of the dynamic programming algorithm for optimization of decision rules relative to coverage. 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.


decision rules coverage dynamic programming 


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  1. 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. 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)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach to optimization of approximate decision rules. Inf. Sci. 221, 403–418 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    An, A., Cercone, N.J.: Rule quality measures improve the accuracy of rule induction: An experimental approach. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 119–129. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository ( (2007),
  6. 6.
    Błaszczyński, J., Słowiński, R., Szeląg, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf. Sci. 181(5), 987–1002 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dembczyński, K., Kotłowski, W., Słowiński, R.: Ender: a statistical framework for boosting decision rules. Data Min. Knowl. Discov. 21(1), 52–90 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Moshkov, M., Piliszczuk, M., Zielosko, B.: Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications. SCI, vol. 145. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  9. 9.
    Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach. SCI, vol. 360. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Nguyen, H.S., Ślęzak, D.: Approximate reducts and association rules - correspondence and complexity results. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 137–145. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Inf. Sci. 177(1), 41–73 (2007)MathSciNetCrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. Int. J. Intell. Syst. 16(1), 13–27 (2001)CrossRefGoogle Scholar
  15. 15.
    Zielosko, B.: Optimization of approximate decision rules relative to coverage. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. (eds.) BDAS 2014. CCIS, vol. 424, pp. 170–179. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Beata Zielosko
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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