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A New Polynomial Time Algorithm for Bayesian Network Structure Learning

  • Sanghack Lee
  • Jihoon Yang
  • Sungyong Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

We propose a new algorithm called SCD  for learning the structure of a Bayesian network. The algorithm is a kind of constraint-based algorithm. By taking advantage of variable ordering, it only requires polynomial time conditional independence tests and learns the exact structure theoretically. A variant which adopts the Bayesian Dirichlet scoring function is also presented for practical purposes. The performance of the algorithms are analyzed in several aspects and compared with other existing algorithms. In addition, we define a new evaluation metric named EP power  which measures the proportion of errors caused by previously made mistakes in the learning sequence, and use the metric for verifying the robustness of the proposed algorithms.

Keywords

Mutual Information Bayesian Network Polynomial Time Algorithm Bayesian Network Model Bayesian Network Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sanghack Lee
    • 1
  • Jihoon Yang
    • 2
  • Sungyong Park
    • 2
  1. 1.Diquest Inc.Seocho-guKorea
  2. 2.Department of Computer ScienceSogang UniversityMapo-guKorea

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