Fast clustering-based weighted twin support vector regression

Gu, Binjie; Fang, Jianwen; Pan, Feng; Bai, Zhonghu

doi:10.1007/s00500-020-04746-6

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…Res-TSVR performed best as compared to other robust TSVR algorithms in terms of robustness to noise and better generalization. Gu et al [47] also proposed a TSVR variant suitable to handle noise called fast clustering-based weighted TSVR (FCWTSVR) which classify the samples into different categories based on their similarities and provides different weights to samples located at different positions. The proposed algorithm performed better than TSVR, ε-TSVR, KNNWTSVR and WL-ε-TSVR.…”

Section: Robust and Sparse Twin Support Vector Regressionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Section: Robust and Sparse Twin Support Vector Regressionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…SVR aims to find a hyperplane to fit the training data. During the training process of SVR, quadratic programming problems (QPPs) need to be solved to obtain the optimal solution of the dual problem [41]. However, the computation time of the QPPs increases with increasing training sample size.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Modelling of Ship Dynamics Using Puma Optimizer Algorithm-Optimized Twin Support Vector Regression

Jiang,

Zhang,

et al. 2024

JMSE

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Ship dynamic models serve as the foundation for designing ship controllers, trajectory planning, and obstacle avoidance. Support vector regression (SVR) is a commonly used nonparametric modelling method for ship dynamics. Achieving high accuracy SVR models requires a substantial amount of training samples. Additionally, as the number of training samples increases, the computational efficiency for solving the quadratic programming problem (QPP) of SVR decreases. Ship controllers demand dynamic models with both high accuracy and computational efficiency. Therefore, to enhance the prediction accuracy and computational efficiency of SVR, this paper proposes a nonparametric modelling method based on twin SVR (TSVR). TSVR replaces a large QPP with a set of smaller QPPs, significantly enhancing generalizability and computational efficiency. To further improve the predictive accuracy of TSVR, the puma optimizer algorithm is employed to determine the optimal hyperparameters. The performance of the proposed method is validated using a Mariner class vessel. Gaussian white noise is introduced into the modelling data to simulate measurement error. The TSVR model accurately predicts various zigzag and turning circle manoeuvring motions under disturbance conditions, demonstrating its robustness and generalizability. Compared to the SVR model, the TSVR model achieves lower root mean square error and computational time, confirming its superior predictive accuracy and computational efficiency.

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“…The -SVQR model mainly focuses on the sparseness of the regression model. Gu et al [ 24 ] also have investigated a new fast clustering-based approach for TSVR to lessen the effect of outliers and noise in the observed data examples by following the prior structural information and successive over relaxation algorithm. Due to influence of noise and outliers in observed real world data, SVR and TSVR regression based models attract in the literature [ 49 , 51 , 77 , 81 ]; Wang et al [ 66 – 68 ].…”

Section: Introductionmentioning

confidence: 99%

On Regularization Based Twin Support Vector Regression with Huber Loss

Gupta

2021

Neural Process Lett

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Twin support vector regression (TSVR) is generally employed with ε-insensitive loss function which is not well capable to handle the noises and outliers. According to the definition, Huber loss function performs as quadratic for small errors and linear for others and shows better performance in comparison to Gaussian loss hence it restrains easily for a different type of noises and outliers. Recently, TSVR with Huber loss (HN-TSVR) has been suggested to handle the noise and outliers. Like TSVR, it is also having the singularity problem which degrades the performance of the model. In this paper, regularized version of HN-TSVR is proposed as regularization based twin support vector regression (RHN-TSVR) to avoid the singularity problem of HN-TSVR by applying the structured risk minimization principle that leads to our model convex and well-posed. This proposed RHN-TSVR model is well capable to handle the noise as well as outliers and avoids the singularity issue. To show the validity and applicability of proposed RHN-TSVR, various experiments perform on several artificial generated datasets having uniform, Gaussian and Laplacian noise as well as on benchmark different real-world datasets and compare with support vector regression, TSVR, ε-asymmetric Huber SVR, ε-support vector quantile regression and HN-TSVR. Here, all benchmark real-world datasets are embedded with a different significant level of noise 0%, 5% and 10% on different reported algorithms with the proposed approach. The proposed algorithm RHN-TSVR is showing better prediction ability on artificial datasets as well as real-world datasets with a different significant level of noise compared to other reported models.

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Fast clustering-based weighted twin support vector regression

Cited by 8 publications

References 40 publications

Comprehensive Review On Twin Support Vector Machines

Comprehensive Review On Twin Support Vector Machines

Nonparametric Modelling of Ship Dynamics Using Puma Optimizer Algorithm-Optimized Twin Support Vector Regression

On Regularization Based Twin Support Vector Regression with Huber Loss

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