An experimental comparison of hybrid algorithms for bayesian network structure learning

Authors:
Maxime Gasse;Alex Aussem;Haytham Elghazel
Affiliations:
Université de Lyon, CNRS, Université Lyon 1, LIRIS, UMR5205, France;Université de Lyon, CNRS, Université Lyon 1, LIRIS, UMR5205, France;Université de Lyon, CNRS, Université Lyon 1, LIRIS, UMR5205, France
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Year:
2012

Citing 19
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks

Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Learning Bayesian networks from data: an information-theory based approach

Artificial Intelligence
Optimal structure identification with greedy search

The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks

The Journal of Machine Learning Research
The max-min hill-climbing Bayesian network structure learning algorithm

Machine Learning
Towards scalable and data efficient learning of Markov boundaries

International Journal of Approximate Reasoning
Bounding the false discovery rate in local Bayesian network learning

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A novel Markov boundary based feature subset selection algorithm

Neurocomputing
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
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure

The Journal of Machine Learning Research
Learning Gaussian graphical models of gene networks with false discovery rate control

EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning

Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
An efficient and scalable algorithm for local Bayesian network structure discovery

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Permutation testing improves Bayesian network learning

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks

Artificial Intelligence in Medicine
Finding consensus Bayesian network structures

Journal of Artificial Intelligence Research

Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from incremental and divide-and-conquer constraint-based methods to learn the parents and children of a target variable. We conduct an experimental comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning, on several benchmarks with various data sizes. Our extensive experiments show that H2PC outperforms MMHC both in terms of goodness of fit to new data and in terms of the quality of the network structure itself, which is closer to the true dependence structure of the data. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.