Local-to-Global Bayesian Network Structure Learning

Tian Gao, Kshitij Fadnis, Murray Campbell
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1193-1202, 2017.

Abstract

We introduce a new local-to-global structure learning algorithm, called graph growing structure learning (GGSL), to learn Bayesian network (BN) structures. GGSL starts at a (random) node and then gradually expands the learned structure through a series of local learning steps. At each local learning step, the proposed algorithm only needs to revisit a subset of the learned nodes, consisting of the local neighborhood of a target, and therefore improves on both memory and time efficiency compared to traditional global structure learning approaches. GGSL also improves on the existing local-to-global learning approaches by removing the need for conflict-resolving AND-rules, and achieves better learning accuracy. We provide theoretical analysis for the local learning step, and show that GGSL outperforms existing algorithms on benchmark datasets. Overall, GGSL demonstrates a novel direction to scale up BN structure learning while limiting accuracy loss.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-gao17a, title = {Local-to-Global {B}ayesian Network Structure Learning}, author = {Tian Gao and Kshitij Fadnis and Murray Campbell}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {1193--1202}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/gao17a/gao17a.pdf}, url = {https://proceedings.mlr.press/v70/gao17a.html}, abstract = {We introduce a new local-to-global structure learning algorithm, called graph growing structure learning (GGSL), to learn Bayesian network (BN) structures. GGSL starts at a (random) node and then gradually expands the learned structure through a series of local learning steps. At each local learning step, the proposed algorithm only needs to revisit a subset of the learned nodes, consisting of the local neighborhood of a target, and therefore improves on both memory and time efficiency compared to traditional global structure learning approaches. GGSL also improves on the existing local-to-global learning approaches by removing the need for conflict-resolving AND-rules, and achieves better learning accuracy. We provide theoretical analysis for the local learning step, and show that GGSL outperforms existing algorithms on benchmark datasets. Overall, GGSL demonstrates a novel direction to scale up BN structure learning while limiting accuracy loss.} }
Endnote
%0 Conference Paper %T Local-to-Global Bayesian Network Structure Learning %A Tian Gao %A Kshitij Fadnis %A Murray Campbell %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-gao17a %I PMLR %P 1193--1202 %U https://proceedings.mlr.press/v70/gao17a.html %V 70 %X We introduce a new local-to-global structure learning algorithm, called graph growing structure learning (GGSL), to learn Bayesian network (BN) structures. GGSL starts at a (random) node and then gradually expands the learned structure through a series of local learning steps. At each local learning step, the proposed algorithm only needs to revisit a subset of the learned nodes, consisting of the local neighborhood of a target, and therefore improves on both memory and time efficiency compared to traditional global structure learning approaches. GGSL also improves on the existing local-to-global learning approaches by removing the need for conflict-resolving AND-rules, and achieves better learning accuracy. We provide theoretical analysis for the local learning step, and show that GGSL outperforms existing algorithms on benchmark datasets. Overall, GGSL demonstrates a novel direction to scale up BN structure learning while limiting accuracy loss.
APA
Gao, T., Fadnis, K. & Campbell, M.. (2017). Local-to-Global Bayesian Network Structure Learning. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:1193-1202 Available from https://proceedings.mlr.press/v70/gao17a.html.

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