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Multiclass support vector machines with example-dependent costs applied to plankton biomass estimation
dc.contributor.author | González González, Pablo | |
dc.contributor.author | Álvarez, Eva | |
dc.contributor.author | Barranquero Tolosa, José | |
dc.contributor.author | Díez Peláez, Jorge | |
dc.contributor.author | González-Quirós Fernández, Rafael | |
dc.contributor.author | Nogueira García, Enrique | |
dc.contributor.author | López Urrutia Lorente, Ángel | |
dc.contributor.author | Coz Velasco, Juan José del | |
dc.date.accessioned | 2014-03-14T07:43:13Z | |
dc.date.available | 2014-03-14T07:43:13Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 24(11), p. 1901-1905 (2013); doi:10.1109/TNNLS.2013.2271535 | |
dc.identifier.issn | 2162-237X | |
dc.identifier.uri | http://hdl.handle.net/10651/24086 | |
dc.description.abstract | In 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.sponsorship | This 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.extent | p. 1901-1905 | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems, 24(11) | |
dc.rights | © 2013 IEEE | |
dc.subject | Cost-sensitive learning | |
dc.subject | SVM | |
dc.title | Multiclass support vector machines with example-dependent costs applied to plankton biomass estimation | |
dc.type | journal article | |
dc.identifier.local | 20140988 | |
dc.identifier.doi | 10.1109/TNNLS.2013.2271535 | |
dc.relation.projectID | MINECO/TIN2011-23558 | |
dc.relation.projectID | FICYT/IB09-059-C2 | |
dc.relation.publisherversion | http://dx.doi.org/10.1109/TNNLS.2013.2271535 | |
dc.rights.accessRights | open access | |
dc.type.hasVersion | AM |
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