article

Knowledge based Least Squares Twin support vector machines

Authors:

Reshma Khemchandani,

Suresh ChandraAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 180, Issue 23

Pages 4606 - 4618

https://doi.org/10.1016/j.ins.2010.07.034

Published: 01 December 2010 Publication History

Abstract

We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of these formulations are capable of generating non-parallel hyperplanes based on real-world data and prior knowledge. We derive the solution of KBLSTWSVM and use it in our computational experiments for comparison against other linear knowledge based SVM formulations. Our experiments show that KBLSTWSVM is a versatile classifier whose solution is extremely simple when compared with other linear knowledge based SVM algorithms.

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Cited By

Tanveer MTiwari AChoudhary RGanaie M(2022)Large-scale pinball twin support vector machinesMachine Language10.1007/s10994-021-06061-z111:10(3525-3548)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10994-021-06061-z
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
Jayadeva Pant HSharma MSoman S(2019)Twin Neural Networks for the classification of large unbalanced datasetsNeurocomputing10.1016/j.neucom.2018.07.089343:C(34-49)Online publication date: 28-May-2019
https://dl.acm.org/doi/10.1016/j.neucom.2018.07.089
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 180, Issue 23

December, 2010

279 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2010.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 December 2010

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Cited By

Tanveer MTiwari AChoudhary RGanaie M(2022)Large-scale pinball twin support vector machinesMachine Language10.1007/s10994-021-06061-z111:10(3525-3548)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10994-021-06061-z
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
Jayadeva Pant HSharma MSoman S(2019)Twin Neural Networks for the classification of large unbalanced datasetsNeurocomputing10.1016/j.neucom.2018.07.089343:C(34-49)Online publication date: 28-May-2019
https://dl.acm.org/doi/10.1016/j.neucom.2018.07.089
Xie X(2017)PAC-Bayes bounds for twin support vector machinesNeurocomputing10.1016/j.neucom.2016.12.052234:C(137-143)Online publication date: 19-Apr-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.12.052
Ding SZhang NZhang XWu F(2017)Twin support vector machineNeural Computing and Applications10.1007/s00521-016-2245-428:11(3119-3130)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s00521-016-2245-4
Tanveer MKhan MHo S(2016)Robust energy-based least squares twin support vector machinesApplied Intelligence10.1007/s10489-015-0751-145:1(174-186)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s10489-015-0751-1
Tomar DAgarwal S(2015)A comparison on multi-class classification methods based on least squares twin support vector machineKnowledge-Based Systems10.1016/j.knosys.2015.02.00981:C(131-147)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1016/j.knosys.2015.02.009
Soman SJayadeva (2015)High performance EEG signal classification using classifiability and the Twin SVMApplied Soft Computing10.1016/j.asoc.2015.01.01830:C(305-318)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.asoc.2015.01.018
Tanveer M(2015)Application of smoothing techniques for linear programming twin support vector machinesKnowledge and Information Systems10.1007/s10115-014-0786-345:1(191-214)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1007/s10115-014-0786-3
Ding SYu JQi BHuang H(2014)An overview on twin support vector machinesArtificial Intelligence Review10.1007/s10462-012-9336-042:2(245-252)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1007/s10462-012-9336-0
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