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

Park, Jinkyoo; Lechevalier, David; Ak, Ronay; Ferguson, Max; Law, Kincho H.; Lee, Yung-Tsun T.; Rachuri, Sudarsan

doi:10.1520/ssms20160008

Cited by 36 publications

(23 citation statements)

References 21 publications

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“…While, GPR uses the kernel to define the covariance of a prior distribution over the target functions and uses the observed training data to define a likelihood function. Here, we also applied radial basis function (RBF) as the kernel for the GPR 56 .…”

Section: Methodsmentioning

confidence: 99%

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Tian

Chen

2021

Sci Rep

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Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.

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Section: Methodsmentioning

confidence: 99%

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Tian

Chen

2021

Sci Rep

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“…After transferring the BN PMML file of the welding process, the Python parser described in Section 4 is used to convert the PMML file into an analytical model in Python using the PyMC3 package [49]. For ease in notation, we term the environment where the BN PMML representation of the welding process is created as the training environment and the environment where this PMML file is used for prediction as testing environment following the notation in [28].…”

Section: Case Study: Welding Processmentioning

confidence: 99%

“…In this paper, we explain the BN PMML schema and demonstrate its usage using a real-world case study of a welding process. It is worth mentioning that PMML v4.3 also has a schema for Gaussian process regression (GPR) [28], which provides probabilistic prediction. There are a few differences between a BN and a GPR.…”

Section: Introductionmentioning

confidence: 99%

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

Nannapaneni

Narayanan

et al. 2018

Smart and Sustainable Manufacturing Systems

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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 paper 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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“…The PMML standard makes this information transfer less troublesome, by defining a standardized way to represent certain types of predictive models. A standardized representation for GPR models was introduced in the latest PMML specification, PMML 4.3 [48].…”

Section: Standardizing Components Of the Data Processing Pipelinementioning

confidence: 99%

A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data

Ferguson

Bhinge²,

Park

et al. 2018

Smart Sustain. Manuf. Syst.

Self Cite

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With recent advances in sensor and computing technology, it is now possible to use real-time machine learning techniques to monitor the state of manufacturing machines. However, making accurate predictions from raw sensor data is still a difficult challenge. In this work, a data processing pipeline is developed to predict the condition of a milling machine tool using raw sensor data. Acceleration and audio time series sensor data is aggregated into blocks that correspond to the individual cutting operations of the Computer Numerical Control (CNC) milling machine. Each block of data is preprocessed using well-known and computationally efficient signal processing techniques. A novel kernel function is proposed to approximate the covariance between preprocessed blocks of time series data. Several Gaussian process regression models are trained to predict tool condition, each with a different covariance kernel function. The model with the novel covariance function outperforms the models that use more common covariance functions. The trained models are expressed using the Predictive Model Markup Language (PMML), where possible, to demonstrate how the predictive model component of the pipeline can be represented in a standardized form. The tool condition model is shown to be accurate, especially when predicting the condition of lightly worn tools.

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

Cited by 36 publications

References 21 publications

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

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

A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data

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