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Conference papers

Fast Bayesian Network Structure Learning using Quasi-Determinism Screening

Thibaud Rahier 1 Sylvain Marié 2 Stéphane Girard 1 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : Learning the structure of Bayesian networks from data is a NP-Hard problem that involves optimization over a super-exponential sized space. In this work, we show that in most real life datasets, a number of the arcs contained in the final structure can be pre-screened at low computational cost with a limited impact on the global graph score. We formalize the identification of these arcs via the notion of quasi-determinism, and propose an associated algorithm that narrows the structure learning task down to a subset of the original variables. We show, on diverse benchmark datasets, that this algorithm exhibits a significant decrease in computational time and complexity for only a little decrease in performance score.
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Submitted on : Friday, January 4, 2019 - 6:52:30 PM
Last modification on : Wednesday, November 3, 2021 - 5:10:27 AM
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  • HAL Id : hal-01691217, version 4



Thibaud Rahier, Sylvain Marié, Stéphane Girard, Florence Forbes. Fast Bayesian Network Structure Learning using Quasi-Determinism Screening. JFRB 2018 - 9èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, May 2018, Toulouse, France. pp.14-24. ⟨hal-01691217v4⟩



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