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

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルNew Frontiers in Artificial Intelligence - JSAI-isAI 2010 Workshops, LENLS, JURISIN, AMBN, ISS, Revised Selected Papers
ページ210-218
ページ数9
DOI
出版ステータスPublished - 2011
外部発表はい
イベント2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010 - Tokyo, Japan
継続期間: 2010 11 182010 11 19

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6797 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010
国/地域Japan
CityTokyo
Period10/11/1810/11/19

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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