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dc.contributor.advisor33
dc.contributor.authorCarrasco, Miguel
dc.contributor.authorVairetti, Carla
dc.contributor.authorLópez, Julio
dc.contributor.authorMaldonado, Sebastián
dc.coverageDOI: 10.1063/5.0260466
dc.date2025
dc.date.accessioned05-01-2026 18:07
dc.date.available05-01-2026 18:07
dc.date.issued33
dc.description.abstractKernel methods are crucial in machine learning due to their ability to model nonlinear relationships in data. Among these, Support Vector Machine (SVM) is widely recognized for its robust performance and appealing optimization properties. In this work, we build upon recent advancements in SVM variants to propose five novel models specifically designed for multiclass learning. In particular, we introduce One-vs-One and One-vs-All versions of the nonparallel hyperplane SVM and improved twin SVM, along with a unified optimization variant (all-together) of the former method for nonlinear multiclass classification. Our empirical evaluation, conducted on 11 datasets and 12 multiclass classifiers, shows the superiority of our methods: four out of the five proposed models rank among the top performers and consistently outperform alternative approaches in terms of balanced accuracy. Additionally, a statistical test was performed, showing significant differences among the classifiers.
dc.identifierhttps://investigadores.uandes.cl/en/publications/a4422831-bbc2-416d-b4d5-d5c30368ef16
dc.identifier.citation33
dc.identifier.uri33
dc.languageeng
dc.language.iso33
dc.publisher33
dc.relation33
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.35 (2025) date: 2025-05-01 nr.5
dc.subject33
dc.title33
dc.titleMulticlass models for nonlinear classification via nonparallel hyperplane support vector machineeng
dc.title33spa
dc.title33und
dc.type33
dc.typeArticleeng
dc.typeArtículospa
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