An Efficient Batch K-Fold Cross-Validation Voronoi Adaptive Sampling Technique for Global Surrogate Modeling

Kaminsky, Andrew L.; Wang, Yi; Pant, Kapil

doi:10.1115/1.4047155

Cited by 23 publications

(4 citation statements)

References 19 publications

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“…It involves splitting the dataset into training and testing sets in order to evaluate the model. The main purpose of its usage is to assess ML models [21]. Model generalization is achieved through the use of cross-validation, specifically K-fold cross-validation.…”

Section: Cross-validationmentioning

confidence: 99%

A Convolutional Deep Neural Network Approach to Predict Autism Spectrum Disorder Based on Eye-Tracking Scan Paths

Alsaidi,

Obeid,

Al-Madi

et al. 2024

Information

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Autism spectrum disorder (ASD) is a developmental disorder that encompasses difficulties in communication (both verbal and non-verbal), social skills, and repetitive behaviors. The diagnosis of autism spectrum disorder typically involves specialized procedures and techniques, which can be time-consuming and expensive. The accuracy and efficiency of the diagnosis depend on the expertise of the specialists and the diagnostic methods employed. To address the growing need for early, rapid, cost-effective, and accurate diagnosis of autism spectrum disorder, there has been a search for advanced smart methods that can automatically classify the disorder. Machine learning offers sophisticated techniques for building automated classifiers that can be utilized by users and clinicians to enhance accuracy and efficiency in diagnosis. Eye-tracking scan paths have emerged as a tool increasingly used in autism spectrum disorder clinics. This methodology examines attentional processes by quantitatively measuring eye movements. Its precision, ease of use, and cost-effectiveness make it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder. The detection of autism spectrum disorder can be achieved by observing the atypical visual attention patterns of children with the disorder compared to typically developing children. This study proposes a deep learning model, known as T-CNN-Autism Spectrum Disorder (T-CNN-ASD), that utilizes eye-tracking scans to classify participants into ASD and typical development (TD) groups. The proposed model consists of two hidden layers with 300 and 150 neurons, respectively, and underwent 10 rounds of cross-validation with a dropout rate of 20%. In the testing phase, the model achieved an accuracy of 95.59%, surpassing the accuracy of other machine learning algorithms such as random forest (RF), decision tree (DT), K-Nearest Neighbors (KNN), and multi-layer perceptron (MLP). Furthermore, the proposed model demonstrated superior performance when compared to the findings reported in previous studies. The results demonstrate that the proposed model can accurately classify children with ASD from those with TD without human intervention.

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Section: Cross-validationmentioning

confidence: 99%

A Convolutional Deep Neural Network Approach to Predict Autism Spectrum Disorder Based on Eye-Tracking Scan Paths

Alsaidi,

Obeid,

Al-Madi

et al. 2024

Information

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“…Gaussian processes [8] are most frequently used and most intensively investigated as a metamodel [9], [10], and are well suited for representing environmental phenomena [2]. However, the accuracy of the model depends decisively on the choice of sample points in the design space [11], [12]. *This work was partially supported by the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany within the ROBDEKON project and the Daimler Truck AG.…”

Section: Introductionmentioning

confidence: 99%

Receding Horizon Cost-Aware Adaptive Sampling for Environmental Monitoring

Westermann

Mayer²,

Petereit

et al. 2023

IEEE Control Syst. Lett.

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In this paper, environmental monitoring by mobile robots is considered where expensive or time-consuming sampling has to be carried out in order to obtain a metamodel of the phenomenon investigated. Due to limited resources, often not only a limited number of samples can be taken, but also the cost and time of the traveled distance between the sample points must be considered. We present an adaptive sampling method that greatly reduces the robot's travel costs for all common sampling criteria with minimal impact on model accuracy. This is achieved by predicting future sample points based on virtual sampling over a horizon in each iteration of the algorithm and suggesting a next sample point after a cost optimization. The algorithm is simulatively evaluated for application to global exploration and reconstruction of unknown phenomena on a variety of randomly generated phenomena. It is shown that our method vastly outperforms standard adaptive sampling.

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“…Leave-One-Out cross-validation for Bayesian model comparison in large data was recently tackled in [11]. [9] introduced an efficient batch multiple-fold cross-Validation Voronoi adaptive sampling technique for global surrogate modeling. [17] introduced a bias-corrected cross-validation estimator for correlated data.…”

Section: Introduction and Notation 1context And Objectivesmentioning

confidence: 99%

Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances

Ginsbourger¹,

Schärer²

2021

Preprint

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We generalize fast Gaussian process leave-one-out formulae to multiplefold cross-validation, highlighting in turn in broad settings the covariance structure of cross-validation residuals. The employed approach, that relies on block matrix inversion via Schur complements, is applied to both Simple and Universal Kriging frameworks. We illustrate how resulting covariances affect model diagnostics and how to properly transform residuals in the first place. Beyond that, we examine how accounting for dependency between such residuals affect cross-validation-based estimation of the scale parameter. It is found in two distinct cases, namely in scale estimation and in broader covariance parameter estimation via pseudolikelihood, that correcting for covariances between cross-validation residuals leads back to maximum likelihood estimation or to an original variation thereof. The proposed fast calculation of Gaussian Process multiplefold cross-validation residuals is implemented and benchmarked against a naive implementation, all in R language. Numerical experiments highlight the accuracy of our approach as well as the substantial speed-ups that it enables. It is noticeable however, as supported by a discussion on the main drivers of computational costs and by a dedicated numerical benchmark, that speed-ups steeply decline as the number of folds (say, all sharing the same size) decreases. Overall, our results enable fast multiplefold cross-validation, have direct consequences in GP model diagnostics, and pave the way to future work on hyperparameter fitting as well as on the promising field of goal-oriented fold design.

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An Efficient Batch K-Fold Cross-Validation Voronoi Adaptive Sampling Technique for Global Surrogate Modeling

Cited by 23 publications

References 19 publications

A Convolutional Deep Neural Network Approach to Predict Autism Spectrum Disorder Based on Eye-Tracking Scan Paths

A Convolutional Deep Neural Network Approach to Predict Autism Spectrum Disorder Based on Eye-Tracking Scan Paths

Receding Horizon Cost-Aware Adaptive Sampling for Environmental Monitoring

Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances

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