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Bagged Structure Learning of Bayesian Networks
Gal Elidan
In: AIStats 2011, 11-13 Apr 2011, Fort Lauderdale, Florida.
AbstractWe present a novel approach for density esti-
mation using Bayesian networks when faced
with scarce and partially observed data. Our
approach relies on Efron's bootstrap frame-
work, and replaces the standard model selec-
tion score by a bootstrap aggregation objec-
tive aimed at sifting out bad decisions dur-
ing the learning procedure. Unlike previ-
ous bootstrap or MCMC based approaches
that are only aimed at recovering specic
structural features, we learn a concrete den-
sity model that can be used for probabilis-
tic generalization. To make use of our ob-
jective when some of the data is missing,
we propose a bagged structural EM proce-
dure that does not incur the heavy com-
putational cost typically associated with a
bootstrap-based approach. We compare our
bagged objective to the Bayesian score and
the Bayesian information criterion (BIC), as
well as other bootstrap-based model selection
objectives, and demonstrate its eectiveness
in improving generalization performance for
varied real-life datasets. [Edit]
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