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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2015: Intelligent Data Engineering and Automated Learning – IDEAL 2015 pp 53–60Cite as

Managing Monotonicity in Classification by a Pruned Random Forest

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9375))

Abstract

In ordinal monotonic classification problems, the class variable should increase according to a subset of explanatory variables. Standard classifiers do not guarantee to produce model that satisfy the monotonicity constraints. Some algorithms have been developed to manage this issue, such as decision trees which have modified the growing and pruning mechanisms. In this contribution we study the suitability of using these mechanisms in the generation of Random Forests. We introduce a simple ensemble pruning mechanism based on the degree of monotonicity. After an exhaustive experimental analysis, we deduce that a Random Forest applied over these problems is able to achieve a slightly better predictive performance than standard algorithms.

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Acknowledgments

This work is supported by the research project TIN2014-57251-P and by a research scholarship, given to the author Sergio Gonzalez by the University of Granada.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Sergio González, Francisco Herrera & Salvador García

Authors
  1. Sergio González
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  2. Francisco Herrera
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  3. Salvador García
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Corresponding author

Correspondence to Salvador García .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

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© 2015 Springer International Publishing Switzerland

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González, S., Herrera, F., García, S. (2015). Managing Monotonicity in Classification by a Pruned Random Forest. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-24834-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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