Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
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The recent explosion of high dimensionality in datasets for several domains has posed a serious challenge to existing Bayesian network structure learning algorithms. Local search methods represent a solution in such spaces but suffer with small datasets. MMHC (Max-Min Hill-Climbing) is one of these local search algorithms where a first phase aims at identifying a possible skeleton by using some statistical association measurements and a second phase performs a greedy search restricted by this skeleton. We propose to replace the first phase, imprecise when the number of data remains relatively very small, by an application of "Perturb and Combine" framework we have already studied in density estimation by using mixtures of bagged trees.