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Entropy-Based Learning of Compositional Models from Data

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Belief Functions: Theory and Applications (BELIEF 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12915))

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Abstract

We investigate learning of belief function compositional models from data using information content and mutual information based on two different definitions of entropy proposed by Jiroušek and Shenoy in 2018 and 2020, respectively. The data consists of 2,310 randomly generated basic assignments of 26 binary variables from a pairwise consistent and decomposable compositional model. We describe results achieved by three simple greedy algorithms for constructing compositional models from the randomly generated low-dimensional basic assignments.

Supported by the Czech Science Foundation – Grant No. 19-06569S (to the first two authors), and by the Harper Professorship (to the third author).

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Notes

  1. 1.

    In our experiments, \(MI_A\) was negative in about 12% of situations, whilst \(MI_P\) was negative only in 0.1% of cases.

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Author information

Authors and Affiliations

  1. The Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czech Republic

    Radim Jiroušek & Václav Kratochvíl

  2. Faculty of Management, Prague University of Economics and Business, Jindřichův Hradec, Czech Republic

    Radim Jiroušek & Václav Kratochvíl

  3. University of Kansas School of Business, Lawrence, KS, 66045, USA

    Prakash P. Shenoy

Authors
  1. Radim Jiroušek
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  2. Václav Kratochvíl
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  3. Prakash P. Shenoy
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Corresponding author

Correspondence to Václav Kratochvíl .

Editor information

Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Thierry Denœux

  2. Université d'Artois, Béthune, France

    Eric Lefèvre

  3. Northwestern Polytechnical University, Xi'an, China

    Zhunga Liu

  4. Université d'Artois, Béthune, France

    Frédéric Pichon

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Jiroušek, R., Kratochvíl, V., Shenoy, P.P. (2021). Entropy-Based Learning of Compositional Models from Data. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-88601-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88600-4

  • Online ISBN: 978-3-030-88601-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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