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Article dans une revue

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

Abstract : Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models. The BN PMML representation is available in PMML v4.3 released by the Data Mining Group. We demonstrate the conversion of analytical models into the BN PMML representation, and the PMML representation of such models into analytical models, through a Python parser. The BNs obtained after parsing PMML representation can then be used to perform Bayesian inference. Finally, we illustrate the developed BN PMML schema for a welding process.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01936950
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : mardi 27 novembre 2018 - 17:39:18
Dernière modification le : vendredi 17 juillet 2020 - 14:59:13

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Saideep Nannapaneni, Anantha Narayanan, Ronay Ak, David Lechevalier, Thurston Sexton, et al.. Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing. Smart and Sustainable Manufacturing Systems, 2018, 2 (1), pp.87-113. ⟨10.1520/SSMS20180018⟩. ⟨hal-01936950⟩

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