@inproceedings{e9d6626693964e898e118026a5db92e5,
title = "Exact structure learning of Bayesian networks by optimal path extension",
abstract = "Bayesian networks are probabilistic graphical models often used in big data analytics. The problem of Bayesian network exact structure learning is to find a network structure that is optimal under certain scoring criteria. The problem is known to be NP-hard and the existing methods are both computationally and memory intensive. In this paper, we introduce a new approach for exact structure learning that leverages relationship between a partial network structure and the remaining variables to constrain the number of ways in which the partial network can be optimally extended. Via experimental results, we show that the method provides up to three times improvement in runtime, and orders of magnitude reduction in memory consumption over the current best algorithms.",
keywords = "Bayesian Networks, Exact Structure Learning, Score-based Learning",
author = "Subhadeep Karan and Jaroslaw Zola",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Big Data, Big Data 2016 ; Conference date: 05-12-2016 Through 08-12-2016",
year = "2016",
doi = "10.1109/BigData.2016.7840588",
language = "English",
series = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "48--55",
editor = "Ronay Ak and George Karypis and Yinglong Xia and Hu, \{Xiaohua Tony\} and Yu, \{Philip S.\} and James Joshi and Lyle Ungar and Ling Liu and Aki-Hiro Sato and Toyotaro Suzumura and Sudarsan Rachuri and Rama Govindaraju and Weijia Xu",
booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
address = "United States",
}