6533b836fe1ef96bd12a146b
RESEARCH PRODUCT
Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)
Yung-tsun T. LeeMax FergusonKincho H. LawSudarsan RachuriRonay AkJinkyoo ParkDavid Lechevaliersubject
Computer sciencecomputer.internet_protocol02 engineering and technologycomputer.software_genreIndustrial and Manufacturing EngineeringArticleSet (abstract data type)[SPI]Engineering Sciences [physics]Kriging020204 information systems0202 electrical engineering electronic engineering information engineeringUncertainty quantificationRepresentation (mathematics)predictive model markup language (PMML)Probabilistic logicdata miningPredictive analyticsXMLComputer Science Applicationspredictive analyticsControl and Systems EngineeringPredictive Model Markup Languagestandards020201 artificial intelligence & image processingData miningcomputerXMLGaussian process regressiondescription
International audience; 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-03-29 |