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Communication Dans Un Congrès Année : 2018

Fast Bayesian Network Structure Learning using Quasi-Determinism Screening

Résumé

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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Dates et versions

hal-01691217 , version 1 (23-01-2018)
hal-01691217 , version 2 (24-01-2018)
hal-01691217 , version 3 (12-03-2018)
hal-01691217 , version 4 (04-01-2019)

Identifiants

  • HAL Id : hal-01691217 , version 4

Citer

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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