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A regression model based on the nearest centroid neighborhood
dc.contributor.author | García, Vicente | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.contributor.author | Marqués Marzal, Ana Isabel | |
dc.contributor.author | Martínez-Peláez, Rafael | |
dc.date.accessioned | 2018-11-30T11:52:17Z | |
dc.date.available | 2018-11-30T11:52:17Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | GARCÍA, V., et al. A regression model based on the nearest centroid neighborhood. Pattern Analysis and Applications, 2018, p. 1-11. | ca_CA |
dc.identifier.issn | 1433-7541 | |
dc.identifier.issn | 1433-755X | |
dc.identifier.uri | http://hdl.handle.net/10234/177818 | |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 12 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer Verlag | ca_CA |
dc.relation.isPartOf | Pattern Analysis and Applications (2018) 21. | ca_CA |
dc.rights | © 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” | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Nearest neighborhood | ca_CA |
dc.subject | Regression analysis | ca_CA |
dc.subject | Surrounding neighborhood | ca_CA |
dc.subject | Symmetry criterion | ca_CA |
dc.title | A regression model based on the nearest centroid neighborhood | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s10044-018-0706-3 | |
dc.relation.projectID | PROMETEOII/2014/062; TIN2013-46522-P | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/article/10.1007/s10044-018-0706-3 | ca_CA |
dc.date.embargoEndDate | 2019-04-14 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
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