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Optimization of Approximate Decision Rules Relative to Length

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Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety (BDAS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 928))

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Abstract

In the paper, a modified dynamic programming approach for optimization of decision rules relative to length is studied. Experimental results connected with length of approximate decision rules, size of a directed acyclic graph, and accuracy of classifiers, are presented.

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References

  1. Alkhalid, A., Amin, T., Chikalov, I., Hussain, S., Moshkov, M., Zielosko, B.: Optimization and analysis of decision trees and rules: dynamic programming approach. Int. J. Gen. Syst. 42(6), 614–634 (2013)

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

  4. 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  MathSciNet  Google Scholar 

  5. Fürnkranz, J.: Separate-and-conquer rule learning. Artif. Intell. Rev. 13(1), 3–54 (1999)

    Article  Google Scholar 

  6. Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach. Studies in Computational Intelligence, vol. 360. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20995-6

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  9. Stańczyk, U., Zielosko, B.: On combining discretisation parameters and attribute ranking for selection of decision rules. In: Polkowski, L. (ed.) IJCRS 2017. LNCS, vol. 10313, pp. 329–349. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60837-2_28

    Chapter  Google Scholar 

  10. Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. Int. J. Intell. Syst. 16(1), 13–27 (2001)

    Article  Google Scholar 

  11. Wróbel, Ł., Sikora, M., Michalak, M.: Rule quality measures settings in classification, regression and survival rule induction - an empirical approach. Fundam. Inform. 149(4), 419–449 (2016)

    Article  MathSciNet  Google Scholar 

  12. Zielosko, B.: Optimization of approximate decision rules relative to coverage. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 170–179. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06932-6_17

    Chapter  Google Scholar 

  13. Zielosko, B.: Optimization of exact decision rules relative to length. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds.) IDT 2017. SIST, vol. 72, pp. 149–158. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59421-7_14

    Chapter  Google Scholar 

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Authors and Affiliations

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

    Beata Zielosko & Krzysztof Żabiński

Authors
  1. Beata Zielosko
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  2. Krzysztof Żabiński
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Corresponding author

Correspondence to Beata Zielosko .

Editor information

Editors and Affiliations

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

    Stanisław Kozielski

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

    Dariusz Mrozek

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

    Paweł Kasprowski

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

    Bożena Małysiak-Mrozek

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

    Daniel Kostrzewa

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Zielosko, B., Żabiński, K. (2018). Optimization of Approximate Decision Rules Relative to Length. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety. BDAS 2018. Communications in Computer and Information Science, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-319-99987-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-99987-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99986-9

  • Online ISBN: 978-3-319-99987-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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