A regression model based on the nearest centroid neighborhood
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Otros documentos de la autoría: García, Vicente; Sánchez Garreta, Josep Salvador; Marqués Marzal, Ana Isabel; Martínez-Peláez, Rafael
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Título
A regression model based on the nearest centroid neighborhoodAutoría
Fecha de publicación
2018Editor
Springer VerlagISSN
1433-7541; 1433-755XCita bibliográfica
GARCÍA, V., et al. A regression model based on the nearest centroid neighborhood. Pattern Analysis and Applications, 2018, p. 1-11.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s10044-018-0706-3Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has ... [+]
The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has also been used successfully for regression problems where the purpose is to predict a continuous numeric label. However, some alternative neighborhood definitions, such as the surrounding neighborhood, have considered that the neighbors should fulfill not only the proximity property, but also a spatial location criterion. In this paper, we explore the use of the k-nearest centroid neighbor rule, which is based on the concept of surrounding neighborhood, for regression problems. Two support vector regression models were executed as reference. Experimentation over a wide collection of real-world data sets and using fifteen odd different values of k demonstrates that the regression algorithm based on the surrounding neighborhood significantly outperforms the traditional k-nearest neighborhood method and also a support vector regression model with a RBF kernel. [-]
Publicado en
Pattern Analysis and Applications (2018) 21.Proyecto de investigación
PROMETEOII/2014/062; TIN2013-46522-PDerechos de acceso
© Springer-Verlag London Ltd., part of Springer Nature 2018
“This is a post-peer-review, pre-copyedit version of an article published in Pattern Analysis and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s10044-018-0706-3”
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info:eu-repo/semantics/openAccess
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info:eu-repo/semantics/openAccess
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