Logo do repositório
 
Publicação

Soft target and functional complexity reduction

2021-09-01Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorVanneschi, Leonardo
dc.contributor.authorCastelli, Mauro
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2021-04-19T22:43:31Z
dc.date.available2024-12-31T01:31:25Z
dc.date.embargoedUntil2023-04-13
dc.date.issued2021-09-01
dc.descriptionVanneschi, L., & Castelli, M. (2021). Soft target and functional complexity reduction: A hybrid regularization method for genetic programming. Expert Systems with Applications, 177, 1-11. [114929]. https://doi.org/10.1016/j.eswa.2021.114929---%ABS1%
dc.description.abstractRegularization is frequently used in supervised machine learning to prevent models from overfitting. This paper tackles the problem of regularization in genetic programming. We apply, for the first time, soft target regularization, a method recently defined for artificial neural networks, to genetic programming. Also, we introduce a novel measure of functional complexity of the genetic programming individuals, aimed at quantifying their degree of curvature. We experimentally demonstrate that both the use of soft target regularization, and the minimization of the complexity during learning, are often able to reduce overfitting, but they are never able to eliminate it. On the other hand, we demonstrate that the integration of these two strategies into a novel hybrid genetic programming system can completely eliminate overfitting, for all the studied test cases. Last but not least, consistently with what found in the literature, we offer experimental evidence of the fact that the size of the genetic programming models has no correlation with their generalization ability.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent619817
dc.identifier.doi10.1016/j.eswa.2021.114929
dc.identifier.issn0957-4174
dc.identifier.otherPURE: 29275368
dc.identifier.otherPURE UUID: ffae247e-09e4-4bd8-bb5b-dcfb33354273
dc.identifier.otherScopus: 85103934730
dc.identifier.otherWOS: 000652669700005
dc.identifier.otherORCID: /0000-0002-8793-1451/work/92451879
dc.identifier.otherORCID: /0000-0003-4732-3328/work/151426781
dc.identifier.urihttp://hdl.handle.net/10362/115822
dc.identifier.urlhttps://www.scopus.com/pages/publications/85103934730
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000652669700005
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
dc.relationData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT
dc.subjectFunctional complexity
dc.subjectGenetic programming
dc.subjectHybrid system
dc.subjectRegularization
dc.subjectSoft target
dc.subjectGeneral Engineering
dc.subjectComputer Science Applications
dc.subjectArtificial Intelligence
dc.titleSoft target and functional complexity reductionen
dc.title.subtitleA hybrid regularization method for genetic programmingen
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage11
degois.publication.titleExpert Systems with Applications
degois.publication.volume177
dspace.entity.typePublication
oaire.awardNumberDSAIPA/DS/0113/2019
oaire.awardNumberDSAIPA/DS/0022/2018
oaire.awardNumberPTDC/CCI-INF/29168/2017
oaire.awardTitleData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicatione750f897-cfb5-46b7-84ff-3b768eeb44f6
relation.isProjectOfPublicationc35c919f-29eb-4019-b809-622c143b6c56
relation.isProjectOfPublication2624e8d1-5a03-474c-b4a2-34987301953a
relation.isProjectOfPublication.latestForDiscoveryc35c919f-29eb-4019-b809-622c143b6c56

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Soft_Target_and_Functional_Complexity_Reduction.pdf
Tamanho:
605.29 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)