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dc.contributor.authorCancela, Brais
dc.contributor.authorBolón-Canedo, Verónica
dc.contributor.authorAlonso-Betanzos, Amparo
dc.date.accessioned2023-11-30T12:58:52Z
dc.date.issued2023-07
dc.identifier.citationB. Cancela, V. Bolón-Canedo, y A. Alonso-Betanzos, «E2E-FS: An End-to-End Feature Selection Method for Neural Networks», IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, n.º 7, pp. 8311-8323, jul. 2023, doi: 10.1109/TPAMI.2022.3228824.es_ES
dc.identifier.issn0162-8828
dc.identifier.urihttp://hdl.handle.net/2183/34392
dc.description.abstract[Abstract]: Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and LASSO variants. Both approaches are focused in different aspects: while the tree-based algorithms provide a clear explanation about which variables are being used to trigger a certain output, LASSO-like approaches sacrifice a detailed explanation in favor of increasing its accuracy. In this paper, we present a novel embedded feature selection algorithm, called End-to-End Feature Selection (E2E-FS), that aims to provide both accuracy and explainability in a clever way. Despite having non-convex regularization terms, our algorithm, similar to the LASSO approach, is solved with gradient descent techniques, introducing some restrictions that force the model to specifically select a maximum number of features that are going to be used subsequently by the classifier. Although these are hard restrictions, the experimental results obtained show that this algorithm can be used with any learning model that is trained using a gradient descent algorithm.es_ES
dc.description.sponsorshipWe also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relation.urihttps://doi.org/10.1109/TPAMI.2022.3228824es_ES
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available online at: https://doi.org/10.1109/TPAMI.2022.3228824es_ES
dc.subjectFeature selectiones_ES
dc.subjectEnd-to-endes_ES
dc.subjectNon-convex problemes_ES
dc.titleE2E-FS: An End-to-End Feature Selection Method for Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2025-07-15es_ES
dc.date.embargoLift2025-07-15
UDC.journalTitleIEEE Transactions on Pattern Analysis and Machine Intelligencees_ES
UDC.volume45es_ES
UDC.issue7es_ES
UDC.startPage8311es_ES
UDC.endPage8323es_ES


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