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dc.creatorSáez, José Antonioes_ES
dc.creatorGalar Idoate, Mikeles_ES
dc.creatorLuengo, Juliánes_ES
dc.creatorHerrera, Franciscoes_ES
dc.description.abstractIn classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, noise filtering, which removes noisy examples from the training data, is one of the most used techniques. This paper proposes a new noise filtering method that combines several filtering strategies in order to increase the accuracy of the classification algorithms used after the filtering process. The filtering is based on the fusion of the predictions of several classifiers used to detect the presence of noise. We translate the idea behind multiple classifier systems, where the information gathered from different models is combined, to noise filtering. In this way, we consider the combination of classifiers instead of using only one to detect noise. Additionally, the proposed method follows an iterative noise filtering scheme that allows us to avoid the usage of detected noisy examples in each new iteration of the filtering process. Finally, we introduce a noisy score to control the filtering sensitivity, in such a way that the amount of noisy examples removed in each iteration can be adapted to the necessities of the practitioner. The first two strategies (use of multiple classifiers and iterative filtering) are used to improve the filtering accuracy, whereas the last one (the noisy score) controls the level of conservation of the filter removing potentially noisy examples. The validity of the proposed method is studied in an exhaustive experimental study. We compare the new filtering method against several state-of-the-art methods to deal with datasets with class noise and study their efficacy in three classifiers with different sensitivity to noise.en
dc.description.sponsorshipAcknowledgment supported by the projects TIN2011-28488, TIN2013-40765-P, P10-TIC-06858 and P11-TIC- 7765. J. A. Sáez was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancement (
dc.relation.ispartofInformation Fusion 27 (2016) 19–32en
dc.rights© 2015 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.subjectFusion of classifiersen
dc.subjectNoisy dataen
dc.subjectClass noiseen
dc.subjectNoise filtersen
dc.titleINFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity controlen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentAutomática y Computaciónes_ES
dc.contributor.departmentAutomatika eta Konputazioaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.type.versionVersión aceptada / Onetsi den bertsioaes

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© 2015 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license.
Except where otherwise noted, this item's license is described as © 2015 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license.

El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
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