Efficiently approximating Markov tree bagging for high-dimensional density estimation

Authors:
Franćois Schnitzler;Sourour Ammar;Philippe Leray;Pierre Geurts;Louis Wehenkel
Affiliations:
Université de Liège, Department of EECS and GIGA-Research, Liège, Belgium;Ecole Polytechnique de l'Université de Nantes, Laboratoire d'Informatique de Nantes Atlantique UMR 6241, France;Ecole Polytechnique de l'Université de Nantes, Laboratoire d'Informatique de Nantes Atlantique UMR 6241, France;Université de Liège, Department of EECS and GIGA-Research, Liège, Belgium;Université de Liège, Department of EECS and GIGA-Research, Liège, Belgium
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Year:
2011

Citing 15
Cited 0

Data structures and network algorithms

Data structures and network algorithms
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
A minimum spanning tree algorithm with inverse-Ackermann type complexity

Journal of the ACM (JACM)
Tractable Bayesian Learning of Tree Belief Networks

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning with mixtures of trees

The Journal of Machine Learning Research
Infinite mixtures of trees

Proceedings of the 24th international conference on Machine learning
Probability Density Estimation by Perturbing and Combining Tree Structured Markov Networks

ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation

The Journal of Machine Learning Research
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning

Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
The Necessity of Bounded Treewidth for Efficient Inference in Bayesian Networks

Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Forest Density Estimation

The Journal of Machine Learning Research
Data analysis with bayesian networks: a bootstrap approach

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On the construction of the inclusion boundary neighbourhood for markov equivalence classes of bayesian network structures

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.