dc.creator | Benítez Peña, Sandra | es |
dc.creator | Blanquero Bravo, Rafael | es |
dc.creator | Carrizosa Priego, Emilio José | es |
dc.creator | Ramírez Cobo, Josefa | es |
dc.date.accessioned | 2021-04-26T11:52:52Z | |
dc.date.available | 2021-04-26T11:52:52Z | |
dc.date.issued | 2018-07-31 | |
dc.identifier.citation | Benítez Peña, S., Blanquero Bravo, R., Carrizosa Priego, E.J. y Ramírez Cobo, J. (2018). On support vector machines under a multiple-cost scenario. Advances in Data Analysis and Classification, 13 (3), 663-682. | |
dc.identifier.issn | 1862-5347 | es |
dc.identifier.issn | 1862-5355 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107821 | |
dc.description.abstract | Support vector machine (SVM) is a powerful tool in binary classification, known to
attain excellent misclassification rates. On the other hand, many realworld classification
problems, such as those found in medical diagnosis, churn or fraud prediction,
involve misclassification costs which may be different in the different classes. However,
it may be hard for the user to provide precise values for such misclassification
costs, whereas it may be much easier to identify acceptable misclassification rates
values. In this paper we propose a novel SVM model in which misclassification costs
are considered by incorporating performance constraints in the problem formulation.
Specifically, our aim is to seek the hyperplane with maximal margin yielding misclassification
rates below given threshold values. Such maximal margin hyperplane
is obtained by solving a quadratic convex problem with linear constraints and integer
variables. The reported numerical experience shows that our model gives the user control
on the misclassification rates in one class (possibly at the expense of an increase
in misclassification rates for the other class) and is feasible in terms of running times. | es |
dc.format | application/pdf | es |
dc.format.extent | 19 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Advances in Data Analysis and Classification, 13 (3), 663-682. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Constrained classification | es |
dc.subject | Misclassification costs | es |
dc.subject | Mixed integer quadratic programming | es |
dc.subject | Sensitivity/specificity trade-off | es |
dc.subject | Support vector machines | es |
dc.title | On support vector machines under a multiple-cost scenario | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa | es |
dc.relation.publisherversion | http://doi.org/10.1007/s11634-018-0330-5 | es |
dc.identifier.doi | 10.1007/s11634-018-0330-5 | es |
dc.contributor.group | Universidad de Sevilla. FQM329: Optimización | es |
dc.journaltitle | Advances in Data Analysis and Classification | es |
dc.publication.volumen | 13 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 663 | es |
dc.publication.endPage | 682 | es |