TY - GEN
T1 - Searching optimal Bayesian network structure on constraint search space
T2 - 2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010
AU - Imoto, Seiya
AU - Kojima, Kaname
AU - Perrier, Eric
AU - Tamada, Yoshinori
AU - Miyano, Satoru
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - Constraint search space
KW - Optimal algorithm
KW - Structural learning
KW - Super-structure
UR - http://www.scopus.com/inward/record.url?scp=82055200644&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82055200644&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25655-4_19
DO - 10.1007/978-3-642-25655-4_19
M3 - Conference contribution
AN - SCOPUS:82055200644
SN - 9783642256547
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 210
EP - 218
BT - New Frontiers in Artificial Intelligence - JSAI-isAI 2010 Workshops, LENLS, JURISIN, AMBN, ISS, Revised Selected Papers
Y2 - 18 November 2010 through 19 November 2010
ER -