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dc.contributor.authorBarbero-Gómez, Javier
dc.contributor.authorGutiérrez, Pedro Antonio
dc.contributor.authorHervás-Martínez, César
dc.date.accessioned2022-05-12T11:12:33Z
dc.date.available2022-05-12T11:12:33Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10396/22914
dc.description.abstractAutomatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen’s Kappa or Spearman’s rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of RMSE=1.0797 for the Retinopathy dataset and RMSE=1.1237 for the Adience dataset averaged over 4 different architectures.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.sourceNeural Processing Letters (2022)es_ES
dc.subjectOrdinal classificationes_ES
dc.subjectConvolutional neural networkses_ES
dc.subjectCumulative link modeles_ES
dc.subjectOrdinal binary decompositiones_ES
dc.titleError-Correcting Output Codes in the Framework of Deep Ordinal Classificationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1007/s11063-022-10824-7es_ES
dc.relation.projectIDGobierno de España. PID2020-115454GB-C22/AEI/10.13039/501100011033es_ES
dc.relation.projectIDJunta de Andalucía. PS-2020-780es_ES
dc.relation.projectIDJunta de Andalucía. UCO-1261651es_ES
dc.relation.projectIDJunta de Andalucía. PY20_00074es_ES
dc.relation.projectIDGobierno de España. PRE2018-085659es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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