Local structure discovery in bayesian networks
2012 (English)In: Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012, 2012, 634-643 p.Conference paper (Refereed)
Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning, the modeler has one or more target variables that are of special interest; he wants to learn the structure near the target variables and is not interested in the rest of the variables. In this paper, we present a score-based local learning algorithm called SLL. We conjecture that our algorithm is theoretically sound in the sense that it is optimal in the limit of large sample size. Empirical results suggest that SLL is competitive when compared to the constraint-based HITON algorithm. We also study the prospects of constructing the network structure for the whole node set based on local results by presenting two algorithms and comparing them to several heuristics.
Place, publisher, year, edition, pages
2012. 634-643 p.
Bayesian network structure, Constraint-based, Exact algorithms, Larger networks, Local structure, Network structures, Small data set, Structure-learning, Algorithms, Artificial intelligence, Computational complexity, Bayesian networks
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-144775ScopusID: 2-s2.0-84886030057ISBN: 978-097490398-9OAI: oai:DiVA.org:kth-144775DiVA: diva2:717728
28th Conference on Uncertainty in Artificial Intelligence, UAI 2012; Catalina Island, CA; United States; 15 August 2012 through 17 August 2012
FunderScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
QC 201405162014-05-162014-04-292014-05-16Bibliographically approved