[en] We consider algorithms for generating Mixtures of Bagged Markov Trees, for density estimation. In problems defined over many variables and when few observations are available, those mixtures generally outperform a single Markov tree maximizing the data likelihood, but are far more expensive to compute. In this paper, we describe new algorithms for approximating such models, with the aim of speeding up learning without sacrificing accuracy. More specifically, we propose to use a filtering step obtained as a by-product from computing a first Markov tree, so as to avoid considering poor candidate edges in the subsequently generated trees. We compare these algorithms (on synthetic data sets) to Mixtures of Bagged Markov Trees, as well as to a single Markov tree derived by the classical Chow-Liu algorithm and to a recently proposed randomized scheme used for building tree mixtures.
Research Center/Unit :
Systèmes et Modélisation
Disciplines :
Computer science
Author, co-author :
Schnitzler, François ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
ammar, sourour
leray, philippe
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Efficiently approximating Markov tree bagging for high-dimensional density estimation
Publication date :
September 2011
Event name :
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Event organizer :
Prof. A. Likas Prof. Y. Theodoridis
Event place :
Athens, Greece
Event date :
from 05-09-2011 to 09-09-2011
Audience :
International
Main work title :
Machine Learning and Knowledge Discovery in Databases, Part III
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture Biomagnet IUAP network of the Belgian Science Policy Office Pascal2 network of excellence of the EC
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