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Metaheuristics for Score-and-Search Bayesian Network Structure Learning

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Advances in Artificial Intelligence (Canadian AI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10233))

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Abstract

Structure optimization is one of the two key components of score-and-search based Bayesian network learning. Extending previous work on ordering-based search (OBS), we present new local search methods for structure optimization which scale to upwards of a thousand variables. We analyze different aspects of local search with respect to OBS that guided us in the construction of our methods. Our improvements include an efficient traversal method for a larger neighbourhood and the usage of more complex metaheuristics (iterated local search and memetic algorithm). We compared our methods against others using test instances generated from real data, and they consistently outperformed the state of the art by a significant margin.

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Notes

  1. 1.

    http://blip.idsia.ch/.

  2. 2.

    The software is available at: https://github.com/kkourin/mobs.

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Author information

Authors and Affiliations

  1. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

    Colin Lee & Peter van Beek

Authors
  1. Colin Lee
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  2. Peter van Beek
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Corresponding author

Correspondence to Peter van Beek .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, Saskatchewan, Canada

    Malek Mouhoub

  2. University of Montreal, Montreal, Québec, Canada

    Philippe Langlais

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Lee, C., van Beek, P. (2017). Metaheuristics for Score-and-Search Bayesian Network Structure Learning. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-57351-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57350-2

  • Online ISBN: 978-3-319-57351-9

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