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Communication Dans Un Congrès Année : 2022

A Pre-Screening Approach for Faster Bayesian Network Structure Learning

Résumé

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.
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Dates et versions

hal-03873684 , version 1 (27-11-2022)
hal-03873684 , version 2 (04-12-2022)

Identifiants

  • HAL Id : hal-03873684 , version 2

Citer

Thibaud Rahier, Sylvain Marié, Florence Forbes. A Pre-Screening Approach for Faster Bayesian Network Structure Learning. ECML-PKDD 2022 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2022, Grenoble, France. pp.1-16. ⟨hal-03873684v2⟩
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