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Título
Robust weighted regression via PAELLA sample weights
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Neurocomputing
Número de la revista
391
Cita Bibliográfica
Castejón-Limas, M., Alaiz-Moreton, H., Fernández-Robles, L., Alfonso-Cendón, J., Fernández-Llamas, C., Sánchez-González, L., & Pérez, H. (2020). Robust weighted regression via PAELLA sample weights. Neurocomputing, 391, 325–333. doi:10.1016/j.neucom.2019.03.108
Editorial
Elsevier
Fecha
2020
ISSN
0925-2312
Resumen
[EN] This paper reports the usage of the occurrence vector provided by the PAELLA algorithm in the context of robust regression. PAELLA was originally conceived as an outlier detection and data cleaning technique. A novel approach is to use this algorithm not for discarding outliers but to generate information related to the reliability of the observations recorded in the dataset. This approach proves to provide successful results when compared to traditional common practice such as outlier removal. A set of experiments using a contrived difficult artificial dataset are described using both neural networks and classical polynomial fitting. Finally, a successful comparison of our approach to two state-of-the-art algorithms proves the benefits of using the PAELLA algorithm in the context of robust regression.
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/MINECO//DPI2016-79960-C3-2-P
URI
DOI
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