Learning Bayesian Network Parameters with Domain Knowledge and Insufficient Data

Zhigao Guo, Xiaoguang Gao, Ruohai Di
; Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:93-104, 2017.

Abstract

To improve the learning accuracy of parameters in a Bayesian network (BN) from limited data, domain knowledge is often incorporated into the learning process as parameter con- straints. Maximum a posteriori (MAP) based methods that use both data and constraints have been studied extensively. Among those methods, the qualitatively maximum a pos- teriori (QMAP) method exhibits high learning performance. In the QMAP method, when the data are limited, estimation from the data often fails to satisfy all the parameter con- straints, which makes the overall QMAP estimation unreliable. To ensure that the QMAP estimation does not violate any given parameter constraint and further improve the learn- ing accuracy, in this paper, we propose a qualitatively maximum a posteriori correction (QMAP-C) estimation algorithm, which regulates QMAP estimation by replacing the data estimation with a further constrained estimation. Experiments show that the proposed al- gorithm outperforms most of the existing parameter learning methods when the parameter constraints are correct.

Cite this Paper


BibTeX
@InProceedings{pmlr-v73-guo17a, title = {Learning Bayesian Network Parameters with Domain Knowledge and Insufficient Data}, author = {Zhigao Guo and Xiaoguang Gao and Ruohai Di}, booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks}, pages = {93--104}, year = {2017}, editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone}, volume = {73}, series = {Proceedings of Machine Learning Research}, month = {20--22 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v73/guo17a/guo17a.pdf}, url = {http://proceedings.mlr.press/v73/guo17a.html}, abstract = {To improve the learning accuracy of parameters in a Bayesian network (BN) from limited data, domain knowledge is often incorporated into the learning process as parameter con- straints. Maximum a posteriori (MAP) based methods that use both data and constraints have been studied extensively. Among those methods, the qualitatively maximum a pos- teriori (QMAP) method exhibits high learning performance. In the QMAP method, when the data are limited, estimation from the data often fails to satisfy all the parameter con- straints, which makes the overall QMAP estimation unreliable. To ensure that the QMAP estimation does not violate any given parameter constraint and further improve the learn- ing accuracy, in this paper, we propose a qualitatively maximum a posteriori correction (QMAP-C) estimation algorithm, which regulates QMAP estimation by replacing the data estimation with a further constrained estimation. Experiments show that the proposed al- gorithm outperforms most of the existing parameter learning methods when the parameter constraints are correct.} }
Endnote
%0 Conference Paper %T Learning Bayesian Network Parameters with Domain Knowledge and Insufficient Data %A Zhigao Guo %A Xiaoguang Gao %A Ruohai Di %B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks %C Proceedings of Machine Learning Research %D 2017 %E Antti Hyttinen %E Joe Suzuki %E Brandon Malone %F pmlr-v73-guo17a %I PMLR %J Proceedings of Machine Learning Research %P 93--104 %U http://proceedings.mlr.press %V 73 %W PMLR %X To improve the learning accuracy of parameters in a Bayesian network (BN) from limited data, domain knowledge is often incorporated into the learning process as parameter con- straints. Maximum a posteriori (MAP) based methods that use both data and constraints have been studied extensively. Among those methods, the qualitatively maximum a pos- teriori (QMAP) method exhibits high learning performance. In the QMAP method, when the data are limited, estimation from the data often fails to satisfy all the parameter con- straints, which makes the overall QMAP estimation unreliable. To ensure that the QMAP estimation does not violate any given parameter constraint and further improve the learn- ing accuracy, in this paper, we propose a qualitatively maximum a posteriori correction (QMAP-C) estimation algorithm, which regulates QMAP estimation by replacing the data estimation with a further constrained estimation. Experiments show that the proposed al- gorithm outperforms most of the existing parameter learning methods when the parameter constraints are correct.
APA
Guo, Z., Gao, X. & Di, R.. (2017). Learning Bayesian Network Parameters with Domain Knowledge and Insufficient Data. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:93-104

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