Bayesian network learning algorithms using structural restrictions
Metadatos
Afficher la notice complèteEditorial
Elsevier
Materia
Bayesian networks Learning algorithms Structural restrictions Inteligencia artificial Artificial intelligence
Date
2006-08-11Referencia bibliográfica
Luis M. de Campos, Javier G. Castellano, Bayesian network learning algorithms using structural restrictions, International Journal of Approximate Reasoning, Volume 45, Issue 2, 2007, Pages 233-254, ISSN 0888-613X, [https://doi.org/10.1016/j.ijar.2006.06.009]
Patrocinador
Spanish Junta de Comunidades de Castilla-La Mancha and Ministerio Educación y Ciencia Projects PBC-02-002 and TIN2004- 06204-C03-02Résumé
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.