Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures

Authors:
Andreas Nägele;Mathäus Dejori;Martin Stetter
Affiliations:
Siemens AG, Corporate Technology, CT IC 4, D-81730 Munich, Germany and Dept. of Computer Science, Technical University of Munich, D-85747 Garching, Germany;Siemens AG, Corporate Technology, CT IC 4, D-81730 Munich, Germany;Siemens AG, Corporate Technology, CT IC 4, D-81730 Munich, Germany
Venue:
ECML '07 Proceedings of the 18th European conference on Machine Learning
Year:
2007

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Abstract

In recent years, Bayesian networks became a popular framework to estimate the dependency structure of a set of variables. However, due to the NP-hardness of structure learning, this is a challenging task and typical state-of-the art algorithms fail to learn in domains with several thousands of variables. In this paper we introduce a novel algorithm, called substructure learning, that reduces the complexity of learning large networks by splitting this task into several small subtasks. Instead of learning one complete network, we estimate the network structure iteratively by learning small subnetworks. Results from several benchmark cases show that substructure learning efficiently reconstructs the network structure in large domains with high accuracy.