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How to Handle Error Bars in Symbolic Regression for Data Mining in Scientific Applications

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Book cover Statistical Learning and Data Sciences (SLDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

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Abstract

Symbolic regression via genetic programming has become a very useful tool for the exploration of large databases for scientific purposes. The technique allows testing hundreds of thousands of mathematical models to find the most adequate to describe the phenomenon under study, given the data available. In this paper, a major refinement is described, which allows handling the problem of the error bars. In particular, it is shown how the use of the geodesic distance on Gaussian manifolds as fitness function allows taking into account the uncertainties in the data, from the beginning of the data analysis process. To exemplify the importance of this development, the proposed methodological improvement has been applied to a set of synthetic data and the results have been compared with more traditional solutions.

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Author information

Authors and Affiliations

  1. Consorzio RFX-Associazione EURATOM-ENEA per la Fusione, Corso Stati Uniti, 4, 35127, Padova, Italy

    A. Murari

  2. Associazione EURATOM-ENEA - University of Rome “Tor Vergata”, Via del Politecnico 1, 00133, Rome, Italy

    E. Peluso, M. Gelfusa, M. Lungaroni & P. Gaudio

Authors
  1. A. Murari
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  2. E. Peluso
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  3. M. Gelfusa
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  4. M. Lungaroni
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  5. P. Gaudio
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Corresponding author

Correspondence to E. Peluso .

Editor information

Editors and Affiliations

  1. University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  3. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

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© 2015 Springer International Publishing Switzerland

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Murari, A., Peluso, E., Gelfusa, M., Lungaroni, M., Gaudio, P. (2015). How to Handle Error Bars in Symbolic Regression for Data Mining in Scientific Applications. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_29

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  • DOI: https://doi.org/10.1007/978-3-319-17091-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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