A Pre-Screening Approach for Faster Bayesian Network Structure Learning
Abstract
Learning the structure of Bayesian networks from data is a NP-Hard problem that involves optimization over a super-exponential sized space. Still, in many real-life datasets a number of the arcs contained in the final structure correspond to strongly related pairs of variables and can be identified efficiently with information-theoretic metrics. In this work, we propose a meta-algorithm to accelerate any existing Bayesian network structure learning method. It contains an additional arc pre-screening step allowing to narrow the structure learning task down to a subset of the original variables, thus reducing the overall problem size. We conduct extensive experiments on both public benchmarks and private industrial datasets, showing that this approach enables a significant decrease in computational time and graph complexity for little to no decrease in performance score.
Cite
Text
Rahier et al. "A Pre-Screening Approach for Faster Bayesian Network Structure Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26419-1_13Markdown
[Rahier et al. "A Pre-Screening Approach for Faster Bayesian Network Structure Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/rahier2022ecmlpkdd-prescreening/) doi:10.1007/978-3-031-26419-1_13BibTeX
@inproceedings{rahier2022ecmlpkdd-prescreening,
title = {{A Pre-Screening Approach for Faster Bayesian Network Structure Learning}},
author = {Rahier, Thibaud and Marié, Sylvain and Forbes, Florence},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2022},
pages = {207-222},
doi = {10.1007/978-3-031-26419-1_13},
url = {https://mlanthology.org/ecmlpkdd/2022/rahier2022ecmlpkdd-prescreening/}
}