TY - GEN
T1 - Learning Bayesian networks with incomplete data by augmentation
AU - Adel, Tameem
AU - De Campos, Cassio P.
PY - 2016/12
Y1 - 2016/12
N2 - We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks with such new approach.
AB - We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks with such new approach.
UR - http://www.scopus.com/inward/record.url?scp=85030454288&partnerID=8YFLogxK
M3 - Conference contribution
T3 - AAAI Conference on Artificial Intelligence
SP - 1684
EP - 1690
BT - Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence
PB - Association for the Advancement of Artificial Intelligence (AAAI)
T2 - 31st AAAI Conference on Artificial Intelligence, AAAI 2017
Y2 - 4 February 2017 through 10 February 2017
ER -