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Solving Regression by Learning an Ensemble of Decision Rules

  • Conference paper
Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

We introduce a novel decision rule induction algorithm for solving the regression problem. There are only few approaches in which decision rules are applied to this type of prediction problems. The algorithm uses a single decision rule as a base classifier in the ensemble. Forward stagewise additive modeling is used in order to obtain the ensemble of decision rules. We consider two types of loss functions, the squared- and absolute-error loss, that are commonly used in regression problems. The minimization of empirical risk based on these loss functions is performed by two optimization techniques, the gradient boosting and the least angle technique. The main advantage of decision rules is their simplicity and good interpretability. The prediction model in the form of an ensemble of decision rules is powerful, which is shown by results of the experiment presented in the paper.

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Krzysztof Dembczyński, Wojciech Kotłowski & Roman Słowiński

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

Authors
  1. Krzysztof Dembczyński
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  2. Wojciech Kotłowski
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  3. Roman Słowiński
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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© 2008 Springer-Verlag Berlin Heidelberg

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Dembczyński, K., Kotłowski, W., Słowiński, R. (2008). Solving Regression by Learning an Ensemble of Decision Rules. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_52

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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