article

A weighted twin support vector regression

Authors:

Laisheng WangAuthors Info & Claims

Knowledge-Based Systems, Volume 33

Pages 92 - 101

https://doi.org/10.1016/j.knosys.2012.03.013

Published: 01 September 2012 Publication History

Abstract

Twin support vector regression (TSVR) is a new regression algorithm, which aims at finding @e-insensitive up- and down-bound functions for the training points. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one in a classical SVR. However, the same penalties are given to the samples in TSVR. In fact, samples in the different positions have different effects on the bound function. Then, we propose a weighted TSVR in this paper, where samples in the different positions are proposed to give different penalties. The final regressor can avoid the over-fitting problem to a certain extent and yield great generalization ability. Numerical experiments on one artificial dataset and nine benchmark datasets demonstrate the feasibility and validity of our proposed algorithm.

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Cited By

Xu CLi BZhang L(2022)Soybean price forecasting based on Lasso and regularized asymmetric ν-TSVRJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21252543:4(4859-4872)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-212525
Zhou KZhang QLi J(2022)TSVMPath: Fast Regularization Parameter Tuning Algorithm for Twin Support Vector MachineNeural Processing Letters10.1007/s11063-022-10870-154:6(5457-5482)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11063-022-10870-1
Singla MShukla K(2020)Robust statistics-based support vector machine and its variants: a surveyNeural Computing and Applications10.1007/s00521-019-04627-632:15(11173-11194)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s00521-019-04627-6
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Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 33, Issue

September, 2012

195 pages

ISSN:0950-7051

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2012.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 September 2012

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Cited By

Xu CLi BZhang L(2022)Soybean price forecasting based on Lasso and regularized asymmetric ν-TSVRJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21252543:4(4859-4872)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-212525
Zhou KZhang QLi J(2022)TSVMPath: Fast Regularization Parameter Tuning Algorithm for Twin Support Vector MachineNeural Processing Letters10.1007/s11063-022-10870-154:6(5457-5482)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11063-022-10870-1
Singla MShukla K(2020)Robust statistics-based support vector machine and its variants: a surveyNeural Computing and Applications10.1007/s00521-019-04627-632:15(11173-11194)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s00521-019-04627-6
Huang HWei X(2020)Multi-core Twin Support Vector Machines Based on Binary PSO OptimizationIntelligent Computing Methodologies10.1007/978-3-030-60796-8_36(420-431)Online publication date: 2-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-60796-8_36
Wang QXu Y(2019)Concave-Convex Programming for Ramp Loss-Based Maximum Margin and Minimum Volume Twin Spheres MachineNeural Processing Letters10.1007/s11063-018-9903-850:2(1093-1114)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11063-018-9903-8
Gupta UGupta D(2019)An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss functionApplied Intelligence10.1007/s10489-019-01465-w49:10(3606-3627)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10489-019-01465-w
Pang XXu Y(2019)A safe screening rule for accelerating weighted twin support vector machineSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3397-123:17(7725-7739)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s00500-018-3397-1
Zhao SFujita H(2019)Predicting the Listing Status of Chinese Listed Companies Using Twin Multi-class Classification Support Vector MachineAdvances and Trends in Artificial Intelligence. From Theory to Practice10.1007/978-3-030-22999-3_5(50-62)Online publication date: 9-Jul-2019
https://dl.acm.org/doi/10.1007/978-3-030-22999-3_5
Abaszade MEffati S(2018)Stochastic support vector regression with probabilistic constraintsApplied Intelligence10.1007/s10489-017-0964-648:1(243-256)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10489-017-0964-6
Xu YWang QPang XTian Y(2018)Maximum margin of twin spheres machine with pinball loss for imbalanced data classificationApplied Intelligence10.1007/s10489-017-0961-948:1(23-34)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10489-017-0961-9
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