Uncertainty in Artificial Intelligence
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An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Changhe Yuan, Brandon Malone
Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents independently. As a result, the heuristic may contain many directed cycles and result in a loose bound. This paper introduces an improved admissible heuristic that tries to avoid directed cycles within small groups of variables. A sparse representation is also introduced to store only the unique optimal parent choices. Empirical results show that the new techniques significantly improved the efficiency and scalability of A* and BFBnB on most of datasets tested in this paper.
Pages: 924-933
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PDF Link: /papers/12/p924-yuan.pdf
AUTHOR = "Changhe Yuan and Brandon Malone",
TITLE = "An Improved Admissible Heuristic for Learning Optimal Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "924--933"

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