Constraint-Based Bayesian Network Structure Learning using Uncertain Experts’ Knowledge

Authors

  • Christophe Gonzales Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
  • Axel Journe CSAI Engie Lab, France
  • Ahmed Mabrouk Engie Lab CRIGEN, France

DOI:

https://doi.org/10.32473/flairs.v34i1.128453

Abstract

Exploiting experts' knowledge can significantly increase the quality of the Bayesian network (BN) structures produced by learning algorithms. However, in practice, experts may not be 100% confident about the opinions they provide. Worst, the latter can also be conflicting. Including such specific knowledge in learning algorithms is therefore complex. In the literature, there exist a few score-based algorithms that can exploit both data and the knowledge about the existence/absence of arcs in the BN. But, as far as we know, no constraint-based learning algorithm is capable of exploiting such knowledge. In this paper, we fill this gap by introducing the mathematical foundations for new independence tests including this kind of information. We provide a new constraint-based algorithm relying on these tests as well as experiments that highlight the robustness of our method and its benefits compared to other constraint-based learning algorithms.

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Published

2021-04-18

How to Cite

Gonzales, C., Journe, A., & Mabrouk, A. (2021). Constraint-Based Bayesian Network Structure Learning using Uncertain Experts’ Knowledge. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128453

Issue

Section

Special Track: Uncertain Reasoning