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PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification

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Book cover Genetic Programming Theory and Practice XIV

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

The EC-Star rule-set representation is extended to allow probabilistic classifiers. This allows the distributed age-layered evolution of probabilistic rule sets. The method is tested on 20 UCI data problems, as well as a larger dataset of arterial blood pressure waveforms. Results show consistent improvement in all cases compared to binary classification rule-sets.

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Authors and Affiliations

  1. Sentient Technologies, San Francisco, CA, USA

    Babak Hodjat, Hormoz Shahrzad & Risto Miikkulainen

  2. University of Oxford, Oxford, UK

    Lawrence Murray & Chris Holmes

Authors
  1. Babak Hodjat
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  2. Hormoz Shahrzad
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  3. Risto Miikkulainen
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  4. Lawrence Murray
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  5. Chris Holmes
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Corresponding author

Correspondence to Babak Hodjat .

Editor information

Editors and Affiliations

  1. Center for the Study of Complex Sys, University of Michigan, Ann Arbor, MI, USA

    Rick Riolo

  2. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

  3. Colorado State University, Fort Collins, CO, USA

    Brian Goldman

  4. Ann Arbor, MI, USA

    Bill Tozier

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Hodjat, B., Shahrzad, H., Miikkulainen, R., Murray, L., Holmes, C. (2018). PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds) Genetic Programming Theory and Practice XIV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-97088-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-97088-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97087-5

  • Online ISBN: 978-3-319-97088-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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