Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
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
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Improved learning of Bayesian networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
The Journal of Machine Learning Research
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
Towards efficient variables ordering for Bayesian networks classifier
Data & Knowledge Engineering
A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Journal of Artificial Intelligence Research
Pattern Recognition Letters
On the use of restrictions for learning bayesian networks
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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The dominant approach for learning Bayesian networks from data is based on the use of a scoring metric, that evaluates the fitness of any given candidate network to the data, and a search procedure, that explores the space of possible solutions. The most efficient methods used in this context are (Iterated) Local Search algorithms. These methods use a predefined neighborhood structure that defines the feasible elementary modifications (local changes) that can be applied to a given solution in order to get another, potentially better solution. If the search space is the set of directed acyclic graphs (dags), the usual choices for local changes are arc addition, arc deletion and arc reversal. In this paper we propose a new definition of neighborhood in the dag space, which uses a modified operator for arc reversal. The motivation for this new operator is the observation that local search algorithms experience problems when some arcs are wrongly oriented. We exemplify the general usefulness of our proposal by means ofa set of experiments with different metrics and different local search methods, including Hill-Climbing and Greedy Randomized Adaptive Search Procedure (GRASP), as well as using several domain problems.