Searching optimal Bayesian network structure on constraint search space: Super-structure approach

Seiya Imoto, Kaname Kojima, Eric Perrier, Yoshinori Tamada, Satoru Miyano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationNew Frontiers in Artificial Intelligence - JSAI-isAI 2010 Workshops, LENLS, JURISIN, AMBN, ISS, Revised Selected Papers
Pages210-218
Number of pages9
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010 - Tokyo, Japan
Duration: 2010 Nov 182010 Nov 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6797 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010
Country/TerritoryJapan
CityTokyo
Period10/11/1810/11/19

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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