Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning Bayesian Networks
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Constrained score+(local)search methods for learning bayesian networks
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
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In the score plus search based Bayesian networks structure learning approach, the most used method is hill climbing (HC), because its implementation is good trade-off between CPU requirements, accuracy of the obtained model, and ease of implementation. Because of these features and to the fact that HC with the classical operators guarantees to obtain a minimal I-map, this approach is really appropriate to deal with high dimensional domains. In this paper we revisited a previously developed HC algorithm (termed constrained HC, or CHC in short) that takes advantage of some scoring metrics properties in order to restrict during the search the parent set of each node. The main drawback of CHC is that there is no warranty of obtaining a minimal I-map, and so the algorithm includes a second stage in which an unconstrained HC is launched by taking as initial solution the one returned by the constrained search stage. In this paper we modify CHC in order to guarantee that its output is a minimal I-map and so the second stage is not needed. In this way we save a considerable amount of CPU time, making the algorithm best suited for high dimensional datasets. A proof is provided about the minimal I-map condition of the returned network, and also computational experiments are reported to show the gain with respect to CPU requirements.