Publication: Improving deep learning performance with missing values via deletion and compensation
dc.affiliation.dpto | UC3M. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA) | es |
dc.contributor.author | Sánchez Morales, Adrián | |
dc.contributor.author | Sancho Gomez, Jose Luis | |
dc.contributor.author | Martinez Garcia, Juan Antonio | |
dc.contributor.author | Figueiras, Aníbal | |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es |
dc.date.accessioned | 2021-10-20T11:18:14Z | |
dc.date.available | 2021-10-20T11:18:14Z | |
dc.date.issued | 2020-09 | |
dc.description | Proceedings of: International Work conference on the Interplay between Natural and Artificial Computation (IWINAC 2015) | en |
dc.description.abstract | Missing 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.sponsorship | The 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.extent | 12 | |
dc.identifier.bibliographicCitation | Sánchez-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.doi | https://doi.org/10.1007/s00521-019-04013-2 | |
dc.identifier.issn | 0941-0643 | |
dc.identifier.publicationfirstpage | 13233 | |
dc.identifier.publicationissue | 17 | |
dc.identifier.publicationlastpage | 13244 | |
dc.identifier.publicationtitle | 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) | en |
dc.identifier.publicationvolume | 32 | |
dc.identifier.uri | https://hdl.handle.net/10016/33476 | |
dc.identifier.uxxi | CC/0000032669 | |
dc.language.iso | eng | |
dc.publisher | Springer Nature | en |
dc.relation.eventdate | 2015-06-01 | |
dc.relation.eventplace | Elx/Elche | es |
dc.relation.eventtitle | International Work conference on the Interplay between Natural and Artificial Computation (IWINAC 2015) | en |
dc.relation.projectID | Gobierno de España. TEC2015-67719-P | es |
dc.rights | © Springer-Verlag London Ltd., part of Springer Nature 2019 | en |
dc.rights.accessRights | open access | en |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Missing values | en |
dc.subject.other | Imputation | en |
dc.subject.other | Classification | en |
dc.subject.other | Deep learning | en |
dc.title | Improving deep learning performance with missing values via deletion and compensation | en |
dc.type | conference paper | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
Files
Original bundle
1 - 1 of 1