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A convex hull-based data selection method for data driven models

dc.contributor.authorKhosravani, Hamid Reza
dc.contributor.authorRuano, Antonio
dc.contributor.authorFerreira, P. M.
dc.date.accessioned2017-04-07T15:55:58Z
dc.date.available2017-04-07T15:55:58Z
dc.date.issued2016-10
dc.description.abstractThe accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models, covering the whole input range in which they will be employed. The convex hull algorithm can be applied as a method for data selection; however the use of conventional implementations of this method in high dimensions, due to its high complexity, is not feasible. In this paper, we propose a randomized approximation convex hull algorithm which can be used for high dimensions in an acceptable execution time, and with low memory requirements. Simulation results show that data selection by the proposed algorithm (coined as ApproxHull) can improve the performance of classification and regression models, in comparison with random data selection. (C) 2016 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.asoc.2016.06.014
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/10400.1/9276
dc.language.isoeng
dc.peerreviewedyes
dc.relation.isbasedonWOS:000380935400039
dc.titleA convex hull-based data selection method for data driven models
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEMS%2F50022%2F2013/PT
oaire.citation.endPage533
oaire.citation.startPage515
oaire.citation.titleApplied Soft Computing
oaire.citation.volume47
oaire.fundingStream5876
person.familyNameKhosravani
person.familyNameRuano
person.familyNameFerreira
person.givenNameHamid Reza
person.givenNameAntonio
person.givenNamePedro
person.identifier.ciencia-idE81C-C56F-D050
person.identifier.orcid0000-0001-7273-5979
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0003-2369-0115
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id16425466700
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
rcaap.typearticle
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relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublicationa81ee154-d26f-41c3-aaa7-90d5e639f2a0
relation.isAuthorOfPublication.latestForDiscoverydd2ad4e5-427f-468c-a272-688fae19ce52
relation.isProjectOfPublication53083a92-791c-473a-8e29-3007fc4bb131
relation.isProjectOfPublication.latestForDiscovery53083a92-791c-473a-8e29-3007fc4bb131

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