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On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

Abstract

Fuzzy rule-based systems constitute a wide spread tool for classification problems, but several proposals may decrease its performance when dealing with multi-class problems. Among existing approaches, the FARC-HD algorithm has excelled as it has shown to achieve accurate and compact classifiers, even in the context of multi-class problems. In this work, we aim to go one step further to improve the behavior of the former algorithm by means of a “divide-and-conquer” approach, via binarization in a one-versus-one scheme. Besides, we will contextualize each binary classifier by adapting the database for each subproblem by means of a granularity learning process to adapt the number of fuzzy labels per variable. Our experimental study, using several datasets from KEEL dataset repository, shows the goodness of the proposed methodology.

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Notes

  1. 1.

    http://www.keel.es/dataset.php.

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Acknowledgments

This work was partially supported by the Spanish Ministry of Science and Technology under project TIN-2012-33856, the Andalusian regional projects P10-TIC-06858 and P11-TIC-7765 and both the University of Jaén and Caja Rural Provincial de Jaén under project UJA2014/06/15.

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Authors and Affiliations

  1. Department of Software Engineering, University of Granada, Granada, Spain

    Pedro Villar

  2. Department of Computer Science, University of Jaén, Jaén, Spain

    Alberto Fernández

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Herrera

Authors
  1. Pedro Villar
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  2. Alberto Fernández
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  3. Francisco Herrera
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Corresponding author

Correspondence to Pedro Villar .

Editor information

Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Villar, P., Fernández, A., Herrera, F. (2016). On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

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