research-article

Wavelet twin support vector machines based on glowworm swarm optimization

Authors:

Yu XueAuthors Info & Claims

Neurocomputing, Volume 225, Issue C

Pages 157 - 163

https://doi.org/10.1016/j.neucom.2016.11.026

Published: 15 February 2017 Publication History

Abstract

Twin support vector machine is a machine learning algorithm developing from standard support vector machine. The performance of twin support vector machine is always better than support vector machine on datasets that have cross regions. Recently proposed wavelet twin support vector machine introduces the wavelet kernel function into twin support vector machine to make the combination of wavelet analysis techniques and twin support vector machine come true. Wavelet twin support vector machine not only expands the range of the kernel function selection, but also greatly improves the generalization ability of twin support vector machine. However, similar with twin support vector machine, wavelet twin support vector machine cannot deal with the parameter selection problem well. Unsuitable parameters reduce the classification capability of the algorithm. In order to solve the parameter selection problem in wavelet twin support vector machine, in this paper, we use glowworm swarm optimization method to optimize the parameters of wavelet twin support vector machine and propose wavelet twin support vector machine based on glowworm swarm optimization. Wavelet twin support vector machine based on glowworm swarm optimization takes the parameters of wavelet twin support vector machine as the position information of glowworms, regards the function to calculate the wavelet twin support vector machine classification accuracy as objective function and starts glowworm swarm optimization algorithm to update the glowworms. The optimal parameters are the position information of glowworms that we get when the glowworm swarm optimal algorithm stops. Wavelet twin support vector machine based on glowworm swarm optimization determines the parameters in wavelet twin support vector machine automatically before the training process to avoid difficulty of parameter selection. Reasonable parameters promote the performance of wavelet twin support vector machine and improve the accuracy. The experimental results on benchmark datasets indicate that the proposed approach is efficient and has high classification accuracy.

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Published In

cover image Neurocomputing

Neurocomputing Volume 225, Issue C

February 2017

214 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 February 2017

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Cited By

Sun ZTao WGao MZhang MSong SWang G(2024)Broiler health monitoring technology based on sound features and random forestEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108849135:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108849
Tao WWang GSun ZXiao SPan LWu QZhang M(2023)Feature optimization method for white feather broiler health monitoring technologyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106372123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106372
Sun ZZhang MLiu JWang JWu QWang G(2023)Research on white feather broiler health monitoring method based on sound detection and transfer learningComputers and Electronics in Agriculture10.1016/j.compag.2023.108319214:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108319
Sun ZGao MZhang MLv MWang G(2023)Research on recognition method of broiler overlapping sounds based on random forest and confidence intervalComputers and Electronics in Agriculture10.1016/j.compag.2023.107801209:COnline publication date: 1-Jun-2023
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Chopra DArora P(2022)Swarm Intelligence in Data ScienceProcedia Computer Science10.1016/j.procs.2022.12.012215:C(104-111)Online publication date: 1-Jan-2022
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Ding SZhang ZSun YShi S(2022)Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithmNeurocomputing10.1016/j.neucom.2022.01.012480:C(146-156)Online publication date: 1-Apr-2022
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Li YSun HYan W(2022)Domain adaptive twin support vector machine learning using privileged informationNeurocomputing10.1016/j.neucom.2021.10.069469:C(13-27)Online publication date: 16-Jan-2022
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Chen ZZhang SShi NLi FWang YCui J(2022)Online state-of-health estimation of lithium-ion battery based on relevance vector machine with dynamic integrationApplied Soft Computing10.1016/j.asoc.2022.109615129:COnline publication date: 1-Nov-2022
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Hazarika BGupta D(2022)Density Weighted Twin Support Vector Machines for Binary Class Imbalance LearningNeural Processing Letters10.1007/s11063-021-10671-y54:2(1091-1130)Online publication date: 1-Apr-2022
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Sahleh ASalahi MEskandari S(2022)SOCP approach to robust twin parametric margin support vector machineApplied Intelligence10.1007/s10489-021-02859-552:8(9174-9192)Online publication date: 1-Jun-2022
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