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Learning Continuous Time Bayesian Network Classifiers Using MapReduce

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  • Villa, Simone
  • Rossetti, Marco

Abstract

Parameter and structural learning on continuous time Bayesian network classifiers are challenging tasks when you are dealing with big data. This paper describes an efficient scalable parallel algorithm for parameter and structural learning in the case of complete data using the MapReduce framework. Two popular instances of classifiers are analyzed, namely the continuous time naive Bayes and the continuous time tree augmented naive Bayes. Details of the proposed algorithm are presented using Hadoop, an open-source implementation of a distributed file system and the MapReduce framework for distributed data processing. Performance evaluation of the designed algorithm shows a robust parallel scaling.

Suggested Citation

  • Villa, Simone & Rossetti, Marco, 2014. "Learning Continuous Time Bayesian Network Classifiers Using MapReduce," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i03).
    • Handle: RePEc:jss:jstsof:v:062:i03
    DOI: http://hdl.handle.net/10.18637/jss.v062.i03
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    References listed on IDEAS

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    1. S. Villa & F. Stella, 2014. "A continuous time Bayesian network classifier for intraday FX prediction," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2079-2092, December.
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    Cited by:

    1. Feng, Haoyuan & Liu, Yue & Wu, Jie & Guo, Kun, 2023. "Financial market spillovers and macroeconomic shocks: Evidence from China," Research in International Business and Finance, Elsevier, vol. 65(C).

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    1. Feng, Haoyuan & Liu, Yue & Wu, Jie & Guo, Kun, 2023. "Financial market spillovers and macroeconomic shocks: Evidence from China," Research in International Business and Finance, Elsevier, vol. 65(C).

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