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Searching Optimal Bayesian Network Structure on Constraint Search Space: Super-Structure Approach

  • Seiya Imoto
  • Kaname Kojima
  • Eric Perrier
  • Yoshinori Tamada
  • Satoru Miyano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6797)

Abstract

Optimal search on Bayesian network structure is known as an NP-hard problem and the applicability of existing optimal algorithms is limited in small Bayesian networks with 30 nodes or so. To learn larger Bayesian networks from observational data, some heuristic algorithms were used, but only a local optimal structure is found and its accuracy is not high in many cases. In this paper, we review optimal search algorithms in a constraint search space; The skeleton of the learned Bayesian network is a sub-graph of the given undirected graph called super-structure. The introduced optimal search algorithm can learn Bayesian networks with several hundreds of nodes when the degree of super-structure is around four. Numerical experiments indicate that constraint optimal search outperforms state-of-the-art heuristic algorithms in terms of accuracy, even if the super-structure is also learned by data.

Keywords

Bayesian networks Structural learning Optimal algorithm Constraint search space Super-structure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Seiya Imoto
    • 1
  • Kaname Kojima
    • 1
  • Eric Perrier
    • 1
  • Yoshinori Tamada
    • 1
  • Satoru Miyano
    • 1
  1. 1.Human Genome Center, Institute of Medical ScienceUniversity of TokyoMinato-kuJapan

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