Fast Bayesian Network Structure Learning using Quasi-Determinism Screening

Thibaud Rahier 1, 2 Sylvain Marié 1 Stéphane Girard 2 Florence Forbes 2
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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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Pré-publication, Document de travail
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Soumis le : lundi 12 mars 2018 - 14:14:51
Dernière modification le : vendredi 1 mars 2019 - 16:23:22
Document(s) archivé(s) le : mercredi 13 juin 2018 - 13:55:29


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  • HAL Id : hal-01691217, version 3


Thibaud Rahier, Sylvain Marié, Stéphane Girard, Florence Forbes. Fast Bayesian Network Structure Learning using Quasi-Determinism Screening. 2018. 〈hal-01691217v3〉



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