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Learning inclusion-optimal chordal graphs
Auvray, Vincent; Wehenkel, Louis
2008In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08)
Peer reviewed
 

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Keywords :
Machine Learning; Graphical Models
Abstract :
[en] Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets.
Research center :
Giga-Systems Biology and Chemical Biology - ULiège
Disciplines :
Computer science
Author, co-author :
Auvray, Vincent;  Université de Liège - ULiège > Département d'électricité, électronique et informatique - Systèmes et Modélisation
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Learning inclusion-optimal chordal graphs
Publication date :
09 July 2008
Event name :
24th Conference on Uncertainty in Artificial Intelligence (UAI-08)
Event place :
Helsinki, Finland
Event date :
from 9-7-2008 to 12-7-2008
Audience :
International
Main work title :
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08)
Publisher :
Morgan Kaufmann
Pages :
18–25
Peer reviewed :
Peer reviewed
Name of the research project :
BIOMOD ARC
Available on ORBi :
since 11 May 2010

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