Show simple item record

dc.contributor.authorGonzalez Abril, Luis
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.authorNúñez Castro, Haydemar
dc.contributor.authorLeal, Yenny
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.identifier.citationGonzalez-Abril, L., Angulo, C., Núñez, H., Leal, Y. Handling binary classification problems with a priority class by using Support Vector Machines. "Applied soft computing", 1 Desembre 2017, vol. 61, p. 661-669.
dc.description.abstract© 2017 Elsevier B.V. A post-processing technique for Support Vector Machine (SVM) algorithms for binary classification problems is introduced in order to obtain adequate accuracy on a priority class (labelled as a positive class). That is, the true positive rate (or recall or sensitivity) is prioritized over the accuracy of the overall classifier. Hence, false negative (or Type I) errors receive greater consideration than false positive (Type II) errors during the construction of the model. This post-processing technique tunes the initial bias term once a solution vector is learned by using standard SVM algorithms in two steps: First, a fixed threshold is given as a lower bound for the recall measure; second, the true negative rate (or specificity) is maximized. Experiments, carried out on eleven standard UCI datasets, show that the modified SVM satisfies the aims for which it has been designed. Furthermore, results are comparable or better than those obtained when other state-of-the-art SVM algorithms and other usual metrics are considered.
dc.format.extent9 p.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshSupport vector machines
dc.subject.otherCost-sensitive SVM
dc.subject.otherPattern recognition
dc.subject.otherPost-processing strategies
dc.subject.otherSupport Vector Machines
dc.titleHandling binary classification problems with a priority class by using Support Vector Machines
dc.subject.lemacAprenentatge automàtic -- Algorismes
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorGonzalez-Abril, L., Angulo, C., Núñez, H., Leal, Y.
upcommons.citation.publicationNameApplied soft computing

Files in this item


This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain