Uncertainty in Artificial Intelligence
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Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks
Brandon Malone, Changhe Yuan
Abstract:
Exact algorithms for learning Bayesian networks guarantee to find provably optimal networks. However, they may fail in difficult learning tasks due to limited time or memory. In this research we adapt several anytime heuristic search-based algorithms to learn Bayesian networks. These algorithms find high-quality solutions quickly, and continually improve the incumbent solution or prove its optimality before resources are exhausted. Empirical results show that the anytime window A* algorithm usually finds higher-quality, often optimal, networks more quickly than other approaches. The results also show that, surprisingly, while generating networks with few parents per variable are structurally simpler, they are harder to learn than complex generating networks with more parents per variable.
Keywords:
Pages: 381-390
PS Link:
PDF Link: /papers/13/p381-malone.pdf
BibTex:
@INPROCEEDINGS{Malone13,
AUTHOR = "Brandon Malone and Changhe Yuan",
TITLE = "Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "381--390"
}


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