Bagged Structure Learning of Bayesian Network

Gal Elidan
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:251-259, 2011.

Abstract

We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only aimed at recovering specific structural features, we learn a concrete density model that can be used for probabilistic generalization. To make use of our objective when some of the data is missing, we propose a bagged structural EM procedure that does not incur the heavy computational cost typically associated with a bootstrap-based approach. We compare our bagged objective to the Bayesian score and the Bayesian information criterion (BIC), as well as other bootstrap-based model selection objectives, and demonstrate its effectiveness in improving generalization performance for varied real-life datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-elidan11a, title = {Bagged Structure Learning of Bayesian Network}, author = {Elidan, Gal}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {251--259}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/elidan11a/elidan11a.pdf}, url = {https://proceedings.mlr.press/v15/elidan11a.html}, abstract = {We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only aimed at recovering specific structural features, we learn a concrete density model that can be used for probabilistic generalization. To make use of our objective when some of the data is missing, we propose a bagged structural EM procedure that does not incur the heavy computational cost typically associated with a bootstrap-based approach. We compare our bagged objective to the Bayesian score and the Bayesian information criterion (BIC), as well as other bootstrap-based model selection objectives, and demonstrate its effectiveness in improving generalization performance for varied real-life datasets.} }
Endnote
%0 Conference Paper %T Bagged Structure Learning of Bayesian Network %A Gal Elidan %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-elidan11a %I PMLR %P 251--259 %U https://proceedings.mlr.press/v15/elidan11a.html %V 15 %X We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only aimed at recovering specific structural features, we learn a concrete density model that can be used for probabilistic generalization. To make use of our objective when some of the data is missing, we propose a bagged structural EM procedure that does not incur the heavy computational cost typically associated with a bootstrap-based approach. We compare our bagged objective to the Bayesian score and the Bayesian information criterion (BIC), as well as other bootstrap-based model selection objectives, and demonstrate its effectiveness in improving generalization performance for varied real-life datasets.
RIS
TY - CPAPER TI - Bagged Structure Learning of Bayesian Network AU - Gal Elidan BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-elidan11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 251 EP - 259 L1 - http://proceedings.mlr.press/v15/elidan11a/elidan11a.pdf UR - https://proceedings.mlr.press/v15/elidan11a.html AB - We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only aimed at recovering specific structural features, we learn a concrete density model that can be used for probabilistic generalization. To make use of our objective when some of the data is missing, we propose a bagged structural EM procedure that does not incur the heavy computational cost typically associated with a bootstrap-based approach. We compare our bagged objective to the Bayesian score and the Bayesian information criterion (BIC), as well as other bootstrap-based model selection objectives, and demonstrate its effectiveness in improving generalization performance for varied real-life datasets. ER -
APA
Elidan, G.. (2011). Bagged Structure Learning of Bayesian Network. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:251-259 Available from https://proceedings.mlr.press/v15/elidan11a.html.

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