The maxmin hillclimbing Bayesian network structure learning algorithm
 Ioannis Tsamardinos,
 Laura E. Brown,
 Constantin F. Aliferis
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Abstract
We present a new algorithm for Bayesian network structure learning, called MaxMin HillClimbing (MMHC). The algorithm combines ideas from local learning, constraintbased, and searchandscore techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesianscoring greedy hillclimbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and stateoftheart algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsllab.org/supplements/mmhc_paper/mmhc_index.html.
 Title
 The maxmin hillclimbing Bayesian network structure learning algorithm
 Journal

Machine Learning
Volume 65, Issue 1 , pp 3178
 Cover Date
 200610
 DOI
 10.1007/s1099400668897
 Print ISSN
 08856125
 Online ISSN
 15730565
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 Bayesian networks
 Graphical models
 Structure learning
 Industry Sectors
 Authors

 Ioannis Tsamardinos ^{(1)}
 Laura E. Brown ^{(1)}
 Constantin F. Aliferis ^{(1)}
 Author Affiliations

 1. Discovery Systems Laboratory, Dept. of Biomedical Informatics, Vanderbilt University, 2209 Garland Avenue, Nashville, TN, 372328340