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Using Genetic Programming to Estimate Performance of Computational Intelligence Models

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Adaptive and Natural Computing Algorithms (ICANNGA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7824))

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Abstract

This paper deals with the problem of choosing the most suitable model for a new data mining task. The metric is proposed on the data mining tasks space, and similar tasks are identified based on this metric. A function estimating models performance on the new task from both the time and error point of view is evolved by means of genetic programming. The approach is verified on data containing results of several hundred thousands machine learning experiments.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 2/25, Prague, Czech Republic

    Jakub Šmíd

  2. Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou víží 2, 18207, Prague, Czech Republic

    Roman Neruda

Authors
  1. Jakub Šmíd
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  2. Roman Neruda
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Editor information

Editors and Affiliations

  1. Départment des Systémes d’Information, Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 105, Lausanne, Switzerland

    Marco Tomassini , Alberto Antonioni , Fabio Daolio  & Pierre Buesser , ,  & 

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Šmíd, J., Neruda, R. (2013). Using Genetic Programming to Estimate Performance of Computational Intelligence Models. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_18

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  • DOI: https://doi.org/10.1007/978-3-642-37213-1_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37212-4

  • Online ISBN: 978-3-642-37213-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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