Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

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This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machinelearning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.
Publisher
AMER SOC TESTING MATERIALS
Issue Date
2017-03
Language
English
Article Type
Article
Citation

Smart and Sustainable Manufacturing Systems, v.1, no.1, pp.121 - 141

ISSN
2520-6478
DOI
10.1520/SSMS20160008
URI
http://hdl.handle.net/10203/251857
Appears in Collection
IE-Journal Papers(저널논문)
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