Improving the Scalability of Optimal Bayesian Network Learning with Frontier Breadth-First Branch and Bound Search

Brandon Malone, Changhe Yuan, Eric Hansen, Susan Bridges

Abstract

Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memoryefficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadth-first branch and bound search that leverages the layered structure of the search graph of this problem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithmstores most of the graph in external memory, such as hard disk, when it does not fit in RAM. Experimental results show that the resulting algorithm solves significantly larger problems than the current state of the art.

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Bibtex

@INPROCEEDINGS{Malone11improving,
author = {Brandon Malone and Changhe Yuan and Eric Hansen and Susan Bridges},
title = {Improving the Scalability of Optimal {B}ayesian Network Learning with Frontier Breadth-First Branch and Bound Search},
booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11)},
year = {2011},
pages = {479--488},
address = {Barcelona, Catalonia, Spain}
}