Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies

  • John Burge
  • Terran Lane
Conference paper

DOI: 10.1007/11871842_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)
Cite this paper as:
Burge J., Lane T. (2006) Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science, vol 4212. Springer, Berlin, Heidelberg

Abstract

Bayesian network structure identification is known to be NP-Hard in the general case. We demonstrate a heuristic search for structure identification based on aggregationhierarchies. The basic idea is to perform initial exhaustive searches on composite “high-level” random variables (RVs) that are created via aggregations of atomic RVs. The results of the high-level searches then constrain a refined search on the atomic RVs. We demonstrate our methods on a challenging real-world neuroimaging domain and show that they consistently yield higher scoring networks when compared to traditional searches, provided sufficient topological complexity is permitted. On simulated data, where ground truth is known and controllable, our methods yield improved classification accuracy and structural precision, but can also result in reduced structural recall on particularly noisy datasets.

Keywords

Bayesian network structure search hierarchy fMRI 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Burge
    • 1
  • Terran Lane
    • 1
  1. 1.Department of Computer ScienceUniversity of New Mexico 

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