Learning Bayesian Networks with Incomplete Data by Augmentation

Adel, T. and de Campos, C. P. (2017) Learning Bayesian Networks with Incomplete Data by Augmentation. In: Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 4-10 Feb 2017, pp. 1684-1690.

Abstract

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Hesham, Dr Tameem Adel
• Authors: Adel, T., and de Campos, C. P.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2159-5399
• Copyright Holders: Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org).
• Publisher Policy: Reproduced in accordance with the publisher copyright policy