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A comparison of four data selection methods for artificial neural networks and support vector machines

2017Conference object Open access
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dc.contributor.authorKhosravani, Hamid Reza
dc.contributor.authorRuano, Antonio
dc.contributor.authorFerreira, P. M.
dc.date.accessioned2019-11-20T15:07:58Z
dc.date.available2019-11-20T15:07:58Z
dc.date.issued2017
dc.description.abstractThe performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of four unsupervised data selection methods including random, convex hull based, entropy based and a hybrid data selection method. These methods were evaluated on eight benchmarks in classification and regression problems. For classification, Support Vector Machines were used, while for the regression problems, Multi-Layer Perceptrons were employed. Additionally, for each problem type, a non-dominated set of Radial Basis Functions Neural Networks were designed, benefiting from a Multi Objective Genetic Algorithm. The simulation results showed that the convex hull based method and the hybrid method involving convex hull and entropy, obtain better performance than the other methods, and that MOGA designed RBFNNs always perform better than the other models. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
dc.description.sponsorshipFCT through IDMEC, under LAETA grant [UID/EMS/50022/2013]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.ifacol.2017.08.1577
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/10400.1/13292
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEvolutionary algorithms
dc.subjectMultiobjective optimization
dc.titleA comparison of four data selection methods for artificial neural networks and support vector machines
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEMS%2F50022%2F2013/PT
oaire.citation.conferencePlaceToulouse, France
oaire.citation.endPage11232
oaire.citation.issue1
oaire.citation.startPage11227
oaire.citation.titleIfac Papersonline
oaire.citation.title20th World Congress of the International-Federation-of-Automatic-Control (Ifac)
oaire.citation.volume50
oaire.fundingStream5876
person.familyNameKhosravani
person.familyNameRuano
person.givenNameHamid Reza
person.givenNameAntonio
person.identifier.orcid0000-0001-7273-5979
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typeconferenceObject
relation.isAuthorOfPublicationdd2ad4e5-427f-468c-a272-688fae19ce52
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
relation.isProjectOfPublication53083a92-791c-473a-8e29-3007fc4bb131
relation.isProjectOfPublication.latestForDiscovery53083a92-791c-473a-8e29-3007fc4bb131

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