IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning Bayesian Networks
The Journal of Machine Learning Research
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Journal of Artificial Intelligence Research
Exact structure discovery in Bayesian networks with less space
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A geometric view on learning Bayesian network structures
International Journal of Approximate Reasoning
Data Mining and Knowledge Discovery
On open questions in the geometric approach to structural learning Bayesian nets
International Journal of Approximate Reasoning
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
A hybrid anytime algorithm for the construction of causal models from sparse data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
MIDAS - an influence diagram for management of mildew in winter wheat
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On local optima in learning bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Characteristic imsets for learning Bayesian network structure
International Journal of Approximate Reasoning
Hi-index | 0.00 |
Greedy Equivalence Search (GES) is nowadays the state of the art algorithm for learning Bayesian networks (BNs) from complete data. However, from a practical point of view, this algorithm may not be efficient enough to deal with data from high dimensionality and/or complex domains. This paper proposes some modifications to GES aimed at increasing its efficiency. Under the faithfulness assumption, the modified algorithms preserve the same theoretical properties of the original one, that is, they recover a perfect map of the target distribution in the large sample limit. Moreover, experimental results confirm that, although the proposed methods carry out a significantly smaller number of computations, the quality of the BNs learned can be compared with those obtained with GES.