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Improving deep learning performance with missing values via deletion and compensation

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorSánchez Morales, Adrián
dc.contributor.authorSancho Gomez, Jose Luis
dc.contributor.authorMartinez Garcia, Juan Antonio
dc.contributor.authorFigueiras, Aníbal
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-10-20T11:18:14Z
dc.date.available2021-10-20T11:18:14Z
dc.date.issued2020-09
dc.descriptionProceedings of: International Work conference on the Interplay between Natural and Artificial Computation (IWINAC 2015)en
dc.description.abstractMissing values in a dataset is one of the most common difficulties in real applications. Many different techniques based on machine learning have been proposed in the literature to face this problem. In this work, the great representation capability of the stacked denoising auto-encoders is used to obtain a new method of imputating missing values based on two ideas: deletion and compensation. This method improves imputation performance by artificially deleting values in the input features and using them as targets in the training process. Nevertheless, although the deletion of samples is demonstrated to be really efficient, it may cause an imbalance between the distributions of the training and the test sets. In order to solve this issue, a compensation mechanism is proposed based on a slight modification of the error function to be optimized. Experiments over several datasets show that the deletion and compensation not only involve improvements in imputation but also in classification in comparison with other classical techniques.en
dc.description.sponsorshipThe work of A. R. Figueiras-Vidal has been partly supported by Grant Macro-ADOBE (TEC 2015-67719-P, MINECO/FEDER&FSE). The work of J.L. Sancho-Gómez has been partly supported by Grant AES 2017 (PI17/00771, MINECO/FEDER).en
dc.format.extent12
dc.identifier.bibliographicCitationSánchez-Morales, A., Sancho-Gómez, J. L., Martínez-García, J. A. & Figueiras-Vidal, A. R. (2020). Improving deep learning performance with missing values via deletion and compensation. In: Neural Computing and Applications (Special Issue on Green and Human Information Technology 2019//International Work conference on the Interplay between Natural and Artificial Computation, IWINAC) 2015, 32(17), Sept. 2020, pp. 13233–13244.en
dc.identifier.doihttps://doi.org/10.1007/s00521-019-04013-2
dc.identifier.issn0941-0643
dc.identifier.publicationfirstpage13233
dc.identifier.publicationissue17
dc.identifier.publicationlastpage13244
dc.identifier.publicationtitleNeural Computing and Applications (Special Issue on Green and Human Information Technology 2019//International Work conference on the Interplay between Natural and Artificial Computation, IWINAC 2015)en
dc.identifier.publicationvolume32
dc.identifier.urihttps://hdl.handle.net/10016/33476
dc.identifier.uxxiCC/0000032669
dc.language.isoeng
dc.publisherSpringer Natureen
dc.relation.eventdate2015-06-01
dc.relation.eventplaceElx/Elchees
dc.relation.eventtitleInternational Work conference on the Interplay between Natural and Artificial Computation (IWINAC 2015)en
dc.relation.projectIDGobierno de España. TEC2015-67719-Pes
dc.rights© Springer-Verlag London Ltd., part of Springer Nature 2019en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherMissing valuesen
dc.subject.otherImputationen
dc.subject.otherClassificationen
dc.subject.otherDeep learningen
dc.titleImproving deep learning performance with missing values via deletion and compensationen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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