A decomposition algorithm for learning Bayesian network structures from data

Authors:
Yifeng Zeng;Jorge Cordero Hernandez
Affiliations:
Dept. of Computer Science, Aalborg University, Aalborg, Denmark;Dept. of Computer Science, Aalborg University, Aalborg, Denmark
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks.