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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Submitted on : Wednesday, January 24, 2018 - 3:09:25 PM
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  • HAL Id : hal-01691217, version 2


Thibaud Rahier, Sylvain Marié, Stéphane Girard, Florence Forbes. Fast Bayesian Network Structure Learning using Quasi-Determinism Screening. 2018. ⟨hal-01691217v2⟩



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