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Efficient learning of Bayesian networks with bounded tree-width
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DOI：10.1016/j.ijar.2016.07.002.png)
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Abstract
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Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [24,29] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. Finding the best k-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an informative score function to characterize the quality of a k-tree. To further improve the quality of the k-trees, we propose a probabilistic hill climbing approach that locally refines the sampled k-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most k. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Bayesian network
Structure learning
Bounded tree-width
Hill climbing
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