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Interpretable support vector machines for functional data

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorMartin-Barragan, Belen
dc.contributor.authorLillo Rodríguez, Rosa Elvira
dc.contributor.authorRomo, Juan
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2021-09-20T08:53:50Z
dc.date.available2021-09-20T08:53:50Z
dc.date.issued2014-01-01
dc.description.abstractSupport Vector Machines (SVMs) is known to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear score usually has many irregular oscillations, making it difficult to interpret. This paper presents a new method, called Interpretable Support Vector Machines for Functional Data, that provides an interpretable classifier with high predictive power. Interpretability might be understood in different ways. The proposed method is flexible enough to cope with different notions of interpretability chosen by the user, thus the obtained coefficient function can be sparse, linear-wise, smooth, etc. The usefulness of the proposed method is shown in real applications getting interpretable classifiers with comparable, sometimes better, predictive ability versus classical SVM.en
dc.description.sponsorshipThe authors thank the anonymous referees and the associate editor for their helpful comments to improve the article. This work has been partially supported by projects MTM2009-14039, ECO2011-25706 of Ministerio de Ciencia e Innovación and FQM-329 of Junta de Andalucía, Spain.en
dc.format.extent10
dc.identifier.bibliographicCitationMartin-Barragan, B., Lillo, R. & Romo, J. (2014). Interpretable support vector machines for functional data. European Journal of Operational Research, 232(1), pp. 146–155.en
dc.identifier.doihttps://doi.org/10.1016/j.ejor.2012.08.017
dc.identifier.issn0377-2217
dc.identifier.publicationfirstpage146
dc.identifier.publicationissue1
dc.identifier.publicationlastpage155
dc.identifier.publicationtitleEuropean Journal of Operational Researchen
dc.identifier.publicationvolume232
dc.identifier.urihttps://hdl.handle.net/10016/33291
dc.identifier.uxxiAR/0000013975
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. MTM2009-14039es
dc.rights© 2012 Elsevier B.V.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherData miningen
dc.subject.otherInterpretabilityen
dc.subject.otherClassificationen
dc.subject.otherLinear programmingen
dc.subject.otherRegularization methodsen
dc.subject.otherFunctional data analysisen
dc.titleInterpretable support vector machines for functional dataen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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