TWSVR: Regression via Twin Support Vector Machine

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

doi:10.1016/j.neunet.2015.10.007

Cited by 64 publications

(13 citation statements)

References 6 publications

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“…A SVR principle was used to establish a regression model via considering the combination of ΔTEAC DPPH , ΔTEAC ABTS and ΔTEAC FRAP as independent variables and predict the inhibition/promotion rate of acrylamide formation. The standard epsilon (ξ) insensitive SVR model used in the present work sets an ξ tube around data points within which errors are discarded using an ξ insensitive loss function40. Under such situation, the selection criteria for an optimized SVR model mainly include the parameter optimization of cost of ξ-SVR ( c ) and γ-function of radial basis function (RBF) ( g ).…”

Section: Resultsmentioning

confidence: 99%

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Huang

Wei

Wang

2016

Sci Rep

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We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.

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Section: Resultsmentioning

confidence: 99%

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Huang

Wei

Wang

2016

Sci Rep

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“…With these, we seek to give the surrogate model the greatest possibility to obtain a suitable performance with the minimum number of plant evaluations. In our implementation, we selected a set

of three machine learning algorithms: a support vector machine regressor (SVM) [ 48 ], a random forest regressor (RF) [ 49 ], and a Bayesian Ridge model (BR) [ 50 ], as shown in Equation ( 13 ), although this methodology can be generalized to any set of machine learning algorithms.…”

Section: Proposed Methods (Asams)mentioning

confidence: 99%

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Duchanoy

Calvo

Moreno-Armendáriz

2020

Sensors

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Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

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“…Zhao et al [ 37 ] extended the concept of twin hyperplanes, and combined the advantages of least squares support vector regression (LSSVR) to generate the estimated regressor, called Twin Least Squares Support Vector Regression (TLSSVR). By observing the model of Peng [ 34 ], Khemchandani et al [ 38 ] believed that only the principle of empirical risk minimization was considered in TSVR. To overcome these difficulties, Shao et al [ 39 ] proposed another twin regression model, called

-TSVR, which considers the principle of structural risk minimization.…”

Section: Introductionmentioning

confidence: 99%

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Zhang

Liu

Wang

et al. 2020

Entropy

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In this article, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy the single noise distribution, including Gaussian distribution and Laplace distribution, but the mixed distribution. Therefore, combining the twin hyperplanes with the fast speed of Least Squares Support Vector Regression (LS-SVR), and then introducing the Gauss–Laplace mixed noise feature, a new regressor, called Gauss-Laplace Twin Least Squares Support Vector Regression (GL-TLSSVR), for the complex noise. Subsequently, we apply the augmented Lagrangian multiplier method to solve the proposed model. Finally, we apply the short-term wind speed data-set to the proposed model. The results of this experiment confirm the effectiveness of our proposed model.

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TWSVR: Regression via Twin Support Vector Machine

Cited by 64 publications

References 6 publications

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

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