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dc.contributor.authorFrías-Blanco, Isvani
dc.contributor.authorDel-Campo-Ávila, José 
dc.contributor.authorRamos-Jiménez, Gonzalo Pascual 
dc.contributor.authorMorales-Bueno, Rafael 
dc.contributor.authorOrtiz-Díaz, Agustín
dc.contributor.authorCaballero-Mota, Yailé
dc.date.accessioned2023-01-23T12:27:19Z
dc.date.available2023-01-23T12:27:19Z
dc.date.issued2015-03-01
dc.identifier.citationI. Frías-Blanco, J. d. Campo-Ávila, G. Ramos-Jiménez, R. Morales-Bueno, A. Ortiz-Díaz and Y. Caballero-Mota, "Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 810-823, 1 March 2015, doi: 10.1109/TKDE.2014.2345382.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/25767
dc.descriptionI. Frías-Blanco, J. d. Campo-Ávila, G. Ramos-Jiménez, R. Morales-Bueno, A. Ortiz-Díaz and Y. Caballero-Mota, "Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 810-823, 1 March 2015 doi: 10.1109/TKDE.2014.2345382. © 2015 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.es_ES
dc.description.abstractIncremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change over time, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naïve Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.es_ES
dc.description.sponsorshipSupported in part by the SESAAME project number TIN2008-06582-C03-03 of the MICINN, Spain. Supported in part by the AUIP (Asociación Universitaria Iberoamericana de Postgrado).es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectAprendizaje - Modelos matemáticoses_ES
dc.subject.otherConcept driftes_ES
dc.subject.otherControl chartes_ES
dc.subject.otherIncremental learninges_ES
dc.subject.otherWeighted moving averagees_ES
dc.titleOnline and Non-Parametric Drift Detection Methods Based on Hoeffding’s Boundses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroFacultad de Comercio y Gestiónes_ES
dc.identifier.doi10.1109/TKDE.2014.2345382
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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