Modified twin support vector regression

Parastalooi, Nafiseh; Amiri, Ali; Aliheidari, Parisa

doi:10.1016/j.neucom.2016.01.105

Cited by 28 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on the importance of the data, given different samples, the penalty is more reasonable. For this reason, various methods [ 42 , 43 , 44 , 45 , 46 ] have been developed in order to study this shortcoming. For example, Xu et al [ 44 ] proposed using the local information present on the sample based on K-nearest neighbor weighted twin support vector regression to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Xu et al [ 44 ] proposed using the local information present on the sample based on K-nearest neighbor weighted twin support vector regression to improve the prediction accuracy. By clustering based on the similarity of training data, Parastalooi et al [ 45 ] proposed an improved twin support vector regression. Ye [ 46 ] proposed an effective weighted Lagrangian

-twin support vector regression (WL-

-TSVR) with quadratic loss function, in which the weight matrix D was introduced in order to reduce the outlier pair to a certain extent Regression of the influence of variables, so as to impose different penalties on samples.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

-twin support vector regression (WL-

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

View full text Add to dashboard Cite

show abstract

“…Zhao et al [26] proposed the notion of twin hyperplanes with the fast speed of least squares support vector regression (LSSVR) yields a new regressor, termed as twin least squares support vector regression (TLSSVR). N.Parastalooi et al [27] proposed modified twin support vector regression (MTSVR) for data regression. Peng [28] proposed a pair of quadratic programming problems (QPP) for directly optimizing TSVR in primal space (PTSVR) based on a series of linear equations.…”

Section: Introductionmentioning

confidence: 99%

Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model

et al. 2020

View full text Add to dashboard Cite

The training algorithm of twin least squares support vector regression (TLSSVR) transforms unequal constraints into equal constraints in a pair of quadratic programming problems, it owns faster computational speed. The classical least squares support vector regression (LSSVR) assumpt that the noise is Gaussian with zero mean and the homoscedastic variance. However, it is found that the noise models in some practical applications satisfy Gaussian distribution with zero mean and heteroscedastic variance. In this paper, the LSSVR is combined with the twin hyperplanes, and then an optimal loss function for Gaussian noise with heteroscedasticity is introduced, which is called the twin least squares support vector regression with heteroscedastic Gaussian noise (TLSSVR-HGN). Like LSSVR, TLSSVR-HGN also lacks sparsity. To analyze the generalization ability of the proposed model, the sparse TLSSVR-HGN (STLSSVR-HGN) is proposed with a simple mechanism. The proposed model has been verified using the artificial data set, several benchmark data sets and actual wind speed data. The experimental results show that TLSSVR-HGN is a better technology than the other algorithms. INDEX TERMS Least squares support vector regression, twin hyperplanes, equality constraints, heteroscedastic gaussian noise.

show abstract

“…In 2010, Peng [15] proposed a twin support vector regression (TSVR), which can be used to establish the prediction model for industrial data. After that, some improved TSVR methods [16][17][18][19][20][21][22] were proposed. By introducing a K-nearest neighbor (KNN) weighted matrix into the optimization problem in TSVR, the modified algorithms [16,19] were proposed to improve the performance of TSVR.…”

Section: Introductionmentioning

confidence: 99%

“…To solve the ill-conditioned problem in the dual objective functions of the traditional TSVR, an implicit Lagrangian formulation for TSVR [18] was proposed to ensure that the matrices in the formulation are always positive semidefinite matrices. Parastalooi et al [21] added a new term into the objective function to obtain structural information of the input data. By comparing with the neural network technology, the disadvantage of neural network is that the optimization process may fall into the local optimum.…”

Section: Introductionmentioning

confidence: 99%

End‐Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Gao

Shen

et al. 2019

Complexity

View full text Add to dashboard Cite

A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.

show abstract

Modified twin support vector regression

Cited by 28 publications

References 42 publications

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

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

Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model

End‐Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Contact Info

Product

Resources

About