dc.contributor.author | Sierra Araujo, Basilio | |
dc.contributor.author | Lazkano Ortega, Elena | |
dc.contributor.author | Irigoyen Garbizu, Itziar | |
dc.contributor.author | Jauregi Iztueta, Ekaitz | |
dc.contributor.author | Mendialdua Beitia, Iñigo | |
dc.date.accessioned | 2024-01-15T17:28:43Z | |
dc.date.available | 2024-01-15T17:28:43Z | |
dc.date.issued | 2011-07-23 | |
dc.identifier.citation | Information Sciences 181(23) : 5158-516 (2011) | es_ES |
dc.identifier.issn | 0020-0255 | |
dc.identifier.uri | http://hdl.handle.net/10810/64000 | |
dc.description.abstract | The nearest neighbor classification method assigns an unclassified point to the class of the nearest case of a set of previously classified points. This rule is independent of the underlying joint distribution of the sample points and their classifications. An extension to this approach is the k-NN method, in which the classification of the unclassified point is made by following a voting criteria within the k nearest points. The method we present here extends the k-NN idea, searching in each class for the k nearest points to the unclassified point, and classifying it in the class which minimizes the mean distance between the unclassified point and the k nearest points within each class. As all classes can take part in the final selection process, we have called the new approach k Nearest Neighbor Equality (k-NNE). Experimental results we obtained empirically show the suitability of the k-NNE algorithm, and its effectiveness suggests that it could be added to the current list of distance based classifiers. | es_ES |
dc.description.sponsorship | This work has been supported by the Basque Country University and by the Basque Government under the research team grant program. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Nearest Neighbor | es_ES |
dc.subject | Supervised Classification | es_ES |
dc.subject | Machine Learning | es_ES |
dc.subject | Non-parametric Pattern Recognition | es_ES |
dc.title | K nearest neighbor equality: giving equal chance to all existing classes | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2011 Elsevier Inc., under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/abs/pii/S0020025511003562 | es_ES |
dc.identifier.doi | 10.1016/j.ins.2011.07.024 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |