Varying coefficient modeling via least squares support vector regression

Shim, Jooyong; Hwang, Chong-Sun

doi:10.1016/j.neucom.2015.02.036

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…In this section we first present the semivarying LS-SVR (Shim and Hwang, 2015) and the varying coefficient LS-SVR and a GCV function for choosing the hyperparameters.…”

Section: Semivarying Coefficient Ls-svrmentioning

confidence: 99%

“…Suykens and Vandewalle (1999) proposed the least squares support vector machine (LS-SVM), which can be seen as the least squares version of SVM (Vapnik, 1995(Vapnik, , 1998.By using LS-SVM the linear equations for solutions and the generalized cross validation (GCV) function for the model selection can be easily induced. Shim and Hwang (2015) proposed a method for fitting the semivarying coefficient regression model using least squares support vector regression (LS-SVR) technique, which analyzes the dynamic relation between a response and features. See for further details, Suykens and Vandewalle (1999), Suykens et al (2001), and .…”

Section: Introductionmentioning

confidence: 99%

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Feature selection in the semivarying coefficient LS-SVR

Hwang¹,

Shim²

2017

Journal of the Korean Data and Information Science Society

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In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.

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“…In this section we first present the semivarying LS-SVR (Shim and Hwang, 2015) and the varying coefficient LS-SVR and a GCV function for choosing the hyperparameters.…”

Section: Semivarying Coefficient Ls-svrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature selection in the semivarying coefficient LS-SVR

Hwang¹,

Shim²

2017

Journal of the Korean Data and Information Science Society

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“…For each data set, the proposed model and the varying coefficient model (VCM) with the least squares-support vector regression (VCM LSSVR, Shim and Hwang, 2015) are applied with the optimal values of the hyperparameters chosen from AIC-type criterion (3.11) and GCV function, respectively. For VCM with the locally weighted regression (VCM LWR) in (2.1) the leave-one-out CV function is used.…”

Section: Numerical Studiesmentioning

confidence: 99%

“…The introductions and current research areas of VCM are found in Hastie and Tibshirani (1993), Hoover et al . (1998), Fan and Zhang (2008) and Shim and Hwang (2015). The problems of estimating coefficient functions and analyzing them appropriately have been studied in many areas of applied Statistics.…”

Section: Introductionmentioning

confidence: 99%

Robust varying coefficient model using L1 regularization

Hwang¹,

Bae²,

Shim³

2016

Journal of the Korean Data and Information Science Society

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In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

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“…Least squares SVM (LS-SVM) is least squares version of SVM and was initially introduced by Suykens and Vanderwalle (1999a). LS-SVM has been proved to be a very appealing and promising method (Suykens et al, 2001;Seok, 2010;Shim and Seok, 2014;Hwang, 2015;Shim and Hwang, 2015).…”

Section: Introductionmentioning

confidence: 99%

LS-SVM for large data sets

Park¹,

Hwang²,

Kim³

2016

Journal of the Korean Data and Information Science Society

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In this paper we propose multiclassification method for large data sets by ensembling least squares support vector machines (LS-SVM) with principal components instead of raw input vector. We use the revised one-vs-all method for multiclassification, which is one of voting scheme based on combining several binary classifications. The revised one-vs-all method is performed by using the hat matrix of LS-SVM ensemble, which is obtained by ensembling LS-SVMs trained using each random sample from the whole large training data. The leave-one-out cross validation (CV) function is used for the optimal values of hyper-parameters which affect the performance of multiclass LS-SVM ensemble. We present the generalized cross validation function to reduce computational burden of leave-one-out CV functions. Experimental results from real data sets are then obtained to illustrate the performance of the proposed multiclass LS-SVM ensemble.

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Varying coefficient modeling via least squares support vector regression

Cited by 5 publications

References 18 publications

Feature selection in the semivarying coefficient LS-SVR

Feature selection in the semivarying coefficient LS-SVR

Robust varying coefficient model using L1 regularization

LS-SVM for large data sets

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