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Learning Bayesian Network Structure for Risky Behavior Modelling

  • Alena Suvorova
  • Alexander Tulupyev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)

Abstract

Bayesian Belief Networks (BBN) provide a comprehensible framework for representing complex systems that allows including expert knowledge and statistical data simultaneously. We explored BBN models for estimating risky behavior rate and compared several network structures, both expert-based and data-based. To learn and evaluate models we used generated behavior data with 9393 observations. We applied both score-based and constraint-based structure learning algorithms. The score-based structures represented better quality scores according to BIC and log-likelihood, prediction quality was almost the same for data-based models and lower but sufficient for expert-based models. Hence, in case of limited data we can reduce computations and apply expert-based structure for solving practical issues.

Keywords

Bayesian belief network Structure learning Machine learning Behavior models Risky behavior 

Notes

Acknowledgements

This work was partially supported by the by RFBR according to the research project No. 16-31-60063 and No. 18-01-00626.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SPIIRASSt. PetersburgRussia
  2. 2.National Research University Higher School of EconomicsSt. PetersburgRussia

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