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dc.contributor.authorBlanco Izquierdo, Víctor 
dc.contributor.authorJapón, Alberto
dc.contributor.authorPuerto, Justo
dc.date.accessioned2021-11-05T13:02:34Z
dc.date.available2021-11-05T13:02:34Z
dc.date.issued2021-10-05
dc.identifier.citationBlanco, V., Japón, A. & Puerto, J. Robust optimal classification trees under noisy labels. Adv Data Anal Classif (2021). [https://doi.org/10.1007/s11634-021-00467-2]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/71316
dc.descriptionThis research has been partially supported by Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion/FEDER grant number PID2020-114594GBC21, Junta de Andalucia projects P18-FR-1422, P18-FR-2369 and projects FEDERUS-1256951, BFQM-322-UGR20, CEI-3-FQM331 and NetmeetData-Ayudas Fundacion BBVA a equipos de investigacion cientifica 2019. The first author was also partially supported by the IMAG-Maria de Maeztu grant CEX2020-001105-M /AEI /10.13039/501100011033.es_ES
dc.description.abstractIn this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. The motivation of this new methodology is based on the superaditive effect of combining together margin based classifiers and outlier detection techniques. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the construction of the tree trying to detect the label noise. Both features are considered and integrated together to design the resulting Optimal Classification Tree.We present a Mixed Integer Non Linear Programming formulation for the problem, suitable to be solved using any of the available off-the-shelf solvers. The model is analyzed and tested on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of our approach. Our computational results show that in most cases the new methodology outperforms both in accuracy and AUC the results of the benchmarks provided by OCT and OCT-H.es_ES
dc.description.sponsorshipSpanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion/FEDER PID2020-114594GBC21es_ES
dc.description.sponsorshipJunta de Andalucia P18-FR-1422 P18-FR-2369es_ES
dc.description.sponsorshipNetmeetData-Ayudas Fundacion BBVA a equipos de investigacion cientifica 2019es_ES
dc.description.sponsorshipIMAG-Maria de Maeztu CEX2020-001105-M /AEI /10.13039/501100011033es_ES
dc.description.sponsorshipFEDERUS-1256951 BFQM-322-UGR20 CEI-3-FQM331es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectMulticlass classificationes_ES
dc.subjectOptimal classification treeses_ES
dc.subjectSupport vector machineses_ES
dc.subjectMixed integer non linear programminges_ES
dc.subjectClassification es_ES
dc.subjectHyperplaneses_ES
dc.titleRobust optimal classification trees under noisy labelses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s11634-021-00467-2
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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