Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Selection of Informative Genes in Gene Expression Based Diagnosis: A Nonparametric Approach
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Optimal structure identification with greedy search
The Journal of Machine Learning Research
On inclusion-driven learning of bayesian networks
The Journal of Machine Learning Research
Finding optimal bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Approximation Methods for Efficient Learning of Bayesian Networks
Proceedings of the 2008 conference on Approximation Methods for Efficient Learning of Bayesian Networks
Learning Bayesian network equivalence classes with Ant Colony optimization
Journal of Artificial Intelligence Research
Learning an L1-regularized Gaussian Bayesian network in the equivalence class space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Finding consensus Bayesian network structures
Journal of Artificial Intelligence Research
International Journal of Approximate Reasoning
Learning AMP chain graphs and some marginal models thereof under faithfulness
International Journal of Approximate Reasoning
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This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima when run repeatedly. When greediness is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild conditions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported showing that KES finds a better local optimum than GES considerably often. Additionally, these results illustrate that the number of different local optima is usually huge.