A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs

  • Anders L. Madsen
  • Frank Jensen
  • Antonio Salmerón
  • Martin Karlsen
  • Helge Langseth
  • Thomas D. Nielsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8754)


The framework of Bayesian networks is a widely popular formalism for performing belief update under uncertainty. Structure restricted Bayesian network models such as the Naive Bayes Model and Tree-Augmented Naive Bayes (TAN) Model have shown impressive performance for solving classification tasks. However, if the number of variables or the amount of data is large, then learning a TAN model from data can be a time consuming task. In this paper, we introduce a new method for parallel learning of a TAN model from large data sets. The method is based on computing the mutual information scores between pairs of variables given the class variable in parallel. The computations are organised in parallel using balanced incomplete block designs. The results of a preliminary empirical evaluation of the proposed method on large data sets show that a significant performance improvement is possible through parallelisation using the method presented in this paper.


Bayesian networks TAN parallel learning 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anders L. Madsen
    • 1
    • 2
  • Frank Jensen
    • 1
  • Antonio Salmerón
    • 3
  • Martin Karlsen
    • 1
  • Helge Langseth
    • 4
  • Thomas D. Nielsen
    • 2
  1. 1.HUGIN EXPERT A/SAalborgDenmark
  2. 2.Department of Computer ScienceAalborg UniversityDenmark
  3. 3.Department of MathematicsUniversity of AlmeríaSpain
  4. 4.Department of Computer and Information ScienceNorwegian University of Science and TechnologyNorway

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