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Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics

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Information Science and Applications 2017 (ICISA 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 424))

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Abstract

This paper evaluates a structure learning method for Bayesian networks called Stepwise Structure Learning with Probabilistic pruning (SSL-Pro). Probabilistic pruning allows this method to obtain appropriate network structures while reducing computational time for structure learning. Computer experiments were conducted to investigate the characteristics of the SSL-Pro. Results showed that the SSL-Pro generally provided favorable performance, and revealed several parameter-setting guidelines to ensure reasonable learning.

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Author information

Authors and Affiliations

  1. Department of Communication Engineering and Informatics, Faculty of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, 182-8585, Japan

    Godai Azuma

  2. Department of Computer Science, National Institute of Technology, Tokyo College, Hachioji, Tokyo, 193-0997, Japan

    Daisuke Kitakoshi & Masato Suzuki

Authors
  1. Godai Azuma
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  2. Daisuke Kitakoshi
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  3. Masato Suzuki
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Corresponding author

Correspondence to Godai Azuma .

Editor information

Editors and Affiliations

  1. Kyonggi University, Seongnam-si, Gyeonggi, Korea (Republic of)

    Kuinam Kim

  2. modelizeIT Inc., CEO and NYU , Stony Brook, New York, USA

    Nikolai Joukov

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© 2017 Springer Nature Singapore Pte Ltd.

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Azuma, G., Kitakoshi, D., Suzuki, M. (2017). Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_62

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  • DOI: https://doi.org/10.1007/978-981-10-4154-9_62

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4153-2

  • Online ISBN: 978-981-10-4154-9

  • eBook Packages: EngineeringEngineering (R0)

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