Bayesian network structure learning by recursive autonomy identification

Authors:
Raanan Yehezkel;Boaz Lerner
Affiliations:
Pattern Analysis and Machine Learning Lab, Department of Electrical & Computer Engineering, Ben-Gurion University, Beer-Sheva, Israel;Pattern Analysis and Machine Learning Lab, Department of Electrical & Computer Engineering, Ben-Gurion University, Beer-Sheva, Israel
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

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Abstract

We propose the recursive autonomy identification (RAI) algorithm for constraint-based Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous sub-structures. The sequence of operations is performed recursively for each autonomous sub-structure while simultaneously increasing the order of the CI test. In comparison to other constraint-based algorithms d-separating structures and then directing the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. Thereby, learning a structure using the RAI algorithm requires a smaller number of high order CI tests. This reduces the complexity and run-time as well as increases structural and prediction accuracies as demonstrated in extensive experimentation.