Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning, 45 (2):
233 - 254 (2007)Eighth European Conference on Symbolic and Quantitative Approaches
to Reasoning with Uncertainty (ECSQARU 2005).DOI: DOI: 10.1016/j.ijar.2006.06.009
Abstract
The use of several types of structural restrictions within algorithms
for learning Bayesian networks is considered. These restrictions
may codify expert knowledge in a given domain, in such a way that
a Bayesian network representing this domain should satisfy them.
The main goal of this paper is to study whether the algorithms for
automatically learning the structure of a Bayesian network from data
can obtain better results by using this prior knowledge. Three types
of restrictions are formally defined: existence of arcs and/or edges,
absence of arcs and/or edges, and ordering restrictions. We analyze
the possible interactions between these types of restrictions and
also how the restrictions can be managed within Bayesian network
learning algorithms based on both the score�+�search and conditional
independence paradigms. Then we particularize our study to two classical
learning algorithms: a local search algorithm guided by a scoring
function, with the operators of arc addition, arc removal and arc
reversal, and the PC algorithm. We also carry out experiments using
these two algorithms on several data sets.