Over-Fitting in Model Selection with Gaussian Process Regression

Mohammed, Rekar O.; Cawley, Gavin C.

doi:10.1007/978-3-319-62416-7_14

Cited by 26 publications

(13 citation statements)

References 7 publications

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“…These unsatisfactory results may be due to the interferences of other ions that affected the Nernstian slope [ 16 , 52 ]. Another concern was the inherent weakness of GP [ 53 ] faced with the complex high dimensional input-output (i.e., 17-dimensional input signal, and eight outputs) of the multi-ion sensing in the hydroponic system. The ANN model was a suitable model to overcome interference problems.…”

Section: Discussionmentioning

confidence: 99%

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

Tuan

Khattak

Zhu

et al. 2020

Sensors

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Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including NO3−, NH4+, K+, Ca2+, Na+, Cl−, H2PO4−, and Mg2+. The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM–feature enrichment (FE)–DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8 mg·L−1 with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5–275 mg·L−1 and 10–80 mg·L−1 had RMSEs of 29.6 and 8.7 mg·L−1 respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system.

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Section: Discussionmentioning

confidence: 99%

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

Tuan

Khattak

Zhu

et al. 2020

Sensors

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“…that there is a larger difference between training and test RMSE for both formation energies and transition levels than KRR. This can be explained by the flexibility of the GPR models which likely causes overfitting when dealing with a small dataset and high dimensional features 83 . The uncertainty vs absolute error plots in Fig.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Universal Machine Learning Framework for Defect Predictions in Zinc Blende Semiconductors

Mannodi-Kanakkithodi

Xiang

Jacoby

et al. 2021

Preprint

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Point defects or impurities are either naturally present in semiconductors or may be intentionally introduced to tune their electronic and optical properties. The nature of impurity energy levels can strongly influence the performance of a semiconductor in applications ranging from solar cells to photodiodes to infrared sensors to qubits for quantum computing. In this work, we develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in group IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed properties, resulting in statistical predictions of the neutral state formation energies and charge transition levels of all possible impurities in the given set of compounds. Kernel ridge regression, Gaussian process regression, and neural networks, with appropriate feature selection and hyperparameter optimization, are seen to yield similar predictive performances and meaningful uncertainty estimates. We apply the ML framework to screen all impurities with lower formation energy than dominant native defects in all group IV, III-V, and II-VI zinc blende semiconductors. An online tool resulting from this work for predicting and visualizing defect properties in semiconductors is made available on github.

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“…GP generally does not suffer from overfitting like other intelligent systems like neural networks (Adedigba et al 2017;Onalo et al 2018a). Nevertheless, overfitting can arise from the marginal likelihood optimization, especially with many hyperparameters (Mohammed and Cawley 2017;Rasmussen and Williams 2006). To solve this problem, cross-validation was used.…”

Section: Gaussian Process Model Developmentmentioning

confidence: 99%

Data-driven model for shear wave transit time prediction for formation evaluation

Onalo

Adedigba

Oloruntobi

et al. 2020

J Petrol Explor Prod Technol

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Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynamic evaluation of reservoir formations. In the exploration and production of oil and gas reservoirs, these sonic logs contain crucial information about the formation. However, shear sonic logs are not acquired in all oil and gas exploration wells. More so, many offset wells are not run with the most recent sonic logging tools capable of measuring both shear and compressional sonic transit times due to the relatively high costs of running such equipment. And in wells where they are deployed, they are run only over limited intervals of the formation. Such wells lack continuous shear wave transit time measurements along the formation. In this study, an exponential Gaussian process model is presented. The model accurately predicts the shear wave transit times in the formations which lack reliable shear wave transit time measurements. The proposed model is developed using an array of well logs, namely depth, density, porosity, gamma ray, and compressional transit time. A Monte Carlo simulation is used to quantify the proposed model uncertainty. The shear sonic transit time predictions are used to estimate some formation deformation properties, namely Young's modulus and Poisson's ratio of a reservoir formation. The results suggest that shear transit time can be represented and predicted by Gaussian-based process model with RMSE, R 2 , and MSE of 11.147, 0.99, and 124.6, respectively. The proposed model provides a reliable and cost-effective tool for oil and gas dynamic formation evaluation. The findings from this study can help for better understanding of shear transit times in formations which do not have multipole sonic logs or where data have been corrupted while logging in the Niger Delta. List of symbolsRHOB Bulk density log (g/cm 3 ) DTCO Compressional wave travel time (µs/ft) Δt c Compressional wave travel time (µs/ft) RESD Deep resistivity log (ohm m) PHIE Effective porosity log (m 3 /m 3 ) den Electron density porosity (g/cc) fl Fluid density (g/cc) , b Formation density (g/cc) Δt fl Formation fluid compressional wave travel time (µs/ft) Δt ma Formation matrix compressional wave travel time (µs/ft) GR min Gamma ray in clean sandstone GR min Gamma ray in shale GR Gamma ray log (gAPI) GR Gamma ray log reading ma Matrix density (g/cc) MAE Mean absolute error MPE Mean percentage error Δt log Measured compressional wave travel time (µs/ft) N Neutron porosity N,fl Neutron response of the matrix Poisson's Ratio Rock porosity Vsh Shale volume DTSM Shear wave travel time (µs/ft) Δt s Shear wave travel time (µs/ft) s Sonic porosity PHIT Total porosity log (m 3 /m 3 ) E Young's modulus (MPa)

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Over-Fitting in Model Selection with Gaussian Process Regression

Cited by 26 publications

References 7 publications

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

Universal Machine Learning Framework for Defect Predictions in Zinc Blende Semiconductors

Data-driven model for shear wave transit time prediction for formation evaluation

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