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Multiclass support vector machines with example-dependent costs applied to plankton biomass estimation

dc.contributor.authorGonzález González, Pablo 
dc.contributor.authorÁlvarez, Eva
dc.contributor.authorBarranquero Tolosa, José 
dc.contributor.authorDíez Peláez, Jorge 
dc.contributor.authorGonzález-Quirós Fernández, Rafael
dc.contributor.authorNogueira García, Enrique
dc.contributor.authorLópez Urrutia Lorente, Ángel
dc.contributor.authorCoz Velasco, Juan José del 
dc.date.accessioned2014-03-14T07:43:13Z
dc.date.available2014-03-14T07:43:13Z
dc.date.issued2013
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 24(11), p. 1901-1905 (2013); doi:10.1109/TNNLS.2013.2271535
dc.identifier.issn2162-237X
dc.identifier.urihttp://hdl.handle.net/10651/24086
dc.description.abstractIn many applications, the mistakes made by an automatic classifier are not equal, they have different costs. These problems may be solved using a cost-sensitive learning approach. The main idea is not to minimize the number of errors, but the total cost produced by such mistakes. This paper presents a new multiclass costsensitive algorithm, in which each example has attached its corresponding misclassification cost. Our proposal is theoretically well-founded and is designed to optimize costsensitive loss functions. This research was motivated by a real-world problem, the biomass estimation of several plankton taxonomic groups. In this particular application, our method improves the performance of traditional multiclass classification approaches that optimize the accuracy
dc.description.sponsorshipThis work was supported in part by the Ministerio de Economía y Competitividad under Grant TIN2011-23558, and FICYT under Grant IB09-059-C2
dc.format.extentp. 1901-1905
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems, 24(11)
dc.rights© 2013 IEEE
dc.subjectCost-sensitive learning
dc.subjectSVM
dc.titleMulticlass support vector machines with example-dependent costs applied to plankton biomass estimation
dc.typejournal article
dc.identifier.local20140988
dc.identifier.doi10.1109/TNNLS.2013.2271535
dc.relation.projectIDMINECO/TIN2011-23558
dc.relation.projectIDFICYT/IB09-059-C2
dc.relation.publisherversionhttp://dx.doi.org/10.1109/TNNLS.2013.2271535
dc.rights.accessRightsopen access
dc.type.hasVersionAM


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