A Pre-Screening Approach for Faster Bayesian Network Structure Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

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.
Fichier principal
Vignette du fichier
sub_368-final.pdf (629.51 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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

Identifiers

  • HAL Id : hal-03873684 , version 2

Cite

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⟩
105 View
51 Download

Share

Gmail Facebook X LinkedIn More