On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications

Laref, Rachid; Losson, Etienne; Sava, Alexandre; Siadat, Maryam

doi:10.1016/j.chemolab.2018.11.011

Cited by 78 publications

(32 citation statements)

References 30 publications

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“…49,50 The SVR regression performance is associated with the proper selection of the parameters, which are 3, C, and g. The value of the 3 is related directly to the number of support vectors. 51 The value of g is determined by the width of the bellshaped curves in the structure of the SVM regression with a Gaussian function (as shown in eqn (7)). In this research, the polynomial kernel function was found to be more accurate than the Gaussian function; therefore, we did not achieve the value of g. On the other hand, the value of C allows the SVM to gain more simple curves as the goal accuracy is obtained.…”

Section: Prediction Performance Of Svmmentioning

confidence: 99%

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary

Jahani²,

Pourbabaki

et al. 2019

RSC Adv.

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Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(3-caprolactone) (PCL)/gelatin (Gt) nanofibers.Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFM MLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving.According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFM MLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.

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Section: Prediction Performance Of Svmmentioning

confidence: 99%

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary

Jahani²,

Pourbabaki

et al. 2019

RSC Adv.

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show abstract

“…The work of [19] shows that the root mean square error of the SVM varies more sharply with the σ parameter than with the C parameter. Our previous study [8] also shows that the σ parameter is much more sensitive than the γ parameter of the LS-SVM.…”

Section: Resultsmentioning

confidence: 99%

“…With a large dataset, parameter tuning could become too expensive to be practical for an office PC due to long computing time and lack of prior knowledge of the parameters. Several alternative methods have been proposed to speed up the search [13][14][15][16][17][18][19][20]. However, they were tested either with only toy examples or with real world data of a few hundreds to a few thousands of samples.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Parameter Selection of SVM and LS-SVM for Regression

Zeng

Tan

Matsunaga

et al. 2019

MAKE

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A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a dataset of more than 200,000 points used for the reconstruction of the global surface ocean CO2 concentration. The results reveal that: (1) the two models are most sensitive to the parameter of the kernel function, which lies in a narrow range for scaled input data; (2) the optimal values of other parameters do not change much for different datasets; and (3) the LS-SVM performs better than the SVM in general. The LS-SVM is recommended, as it has less parameters to be tuned and yields a smaller bias. Nevertheless, the SVM has advantages of consuming less computer resources and taking less time to train. The results suggest initial parameter guesses for using the models.

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“…The performance of nonlinear SVR is highly dependent on the selection of hyperparameters (e.g., ϵ, C, and σ for RBF-based SVR) [32][33][34]. Careful tuning of these parameters is essential for improving the performance of the SVR model.…”

Section: Implementation Of Optimization In Svr Modelsmentioning

confidence: 99%

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

Hu²,

Liu

2020

SN Appl. Sci.

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On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications

Cited by 78 publications

References 30 publications

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Generalization of Parameter Selection of SVM and LS-SVM for Regression

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

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