Interpretable support vector machines for functional data

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dc.contributor.author Martin-Barragan, Belen
dc.contributor.author Lillo Rodríguez, Rosa Elvira
dc.contributor.author Romo, Juan
dc.date.accessioned 2021-09-20T08:53:50Z
dc.date.available 2021-09-20T08:53:50Z
dc.date.issued 2014-01-01
dc.identifier.bibliographicCitation Martin-Barragan, B., Lillo, R. & Romo, J. (2014). Interpretable support vector machines for functional data. European Journal of Operational Research, 232(1), pp. 146–155.
dc.identifier.issn 0377-2217
dc.identifier.uri http://hdl.handle.net/10016/33291
dc.description.abstract Support 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.
dc.description.sponsorship The 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.
dc.format.extent 10
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2012 Elsevier B.V.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Data mining
dc.subject.other Interpretability
dc.subject.other Classification
dc.subject.other Linear programming
dc.subject.other Regularization methods
dc.subject.other Functional data analysis
dc.title Interpretable support vector machines for functional data
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi https://doi.org/10.1016/j.ejor.2012.08.017
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. MTM2009-14039
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 146
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 155
dc.identifier.publicationtitle European Journal of Operational Research
dc.identifier.publicationvolume 232
dc.identifier.uxxi AR/0000013975
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
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