Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor

As an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if it is possible to find a good value of the parmeter k for each example according to their attribute values. Or at least, if there is a pattern for the parameter k in the original s...

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Detalles Bibliográficos
Autores: Ferrer Troyano, Francisco Javier, Aguilar Ruiz, Jesús Salvador, Riquelme Santos, José Cristóbal
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2003
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/39230
Acceso en línea:http://hdl.handle.net/11441/39230
https://doi.org/10.1007/3-540-44862-4_83
Access Level:acceso abierto
Palabra clave:Nearest Neighbor
Local Adaptive Nearest Neighbor
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spelling Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest NeighborFerrer Troyano, Francisco JavierAguilar Ruiz, Jesús SalvadorRiquelme Santos, José CristóbalNearest NeighborLocal Adaptive Nearest NeighborAs an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if it is possible to find a good value of the parmeter k for each example according to their attribute values. Or at least, if there is a pattern for the parameter k in the original search space. We have carried out different approaches based onthe Nearest Neighbor technique and calculated the prediction accuracy for a group of databases from the UCI repository. Based on the experimental results of our study, we can state that, in general, it is not possible to know a priori a specific value of k to correctly classify an unseen example.Lenguajes y Sistemas Informáticos2003info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/39230https://doi.org/10.1007/3-540-44862-4_83reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputational Science — ICCS 2003, Lecture Notes in Computer Science, Volume 2658, pp 766-773 (2003)info:eu-repo/semantics/openAccessoai:idus.us.es:11441/392302026-06-17T12:51:07Z
dc.title.none.fl_str_mv Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
title Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
spellingShingle Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
Ferrer Troyano, Francisco Javier
Nearest Neighbor
Local Adaptive Nearest Neighbor
title_short Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
title_full Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
title_fullStr Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
title_full_unstemmed Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
title_sort Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor
dc.creator.none.fl_str_mv Ferrer Troyano, Francisco Javier
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
author Ferrer Troyano, Francisco Javier
author_facet Ferrer Troyano, Francisco Javier
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
author_role author
author2 Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
author2_role author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Nearest Neighbor
Local Adaptive Nearest Neighbor
topic Nearest Neighbor
Local Adaptive Nearest Neighbor
description As an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if it is possible to find a good value of the parmeter k for each example according to their attribute values. Or at least, if there is a pattern for the parameter k in the original search space. We have carried out different approaches based onthe Nearest Neighbor technique and calculated the prediction accuracy for a group of databases from the UCI repository. Based on the experimental results of our study, we can state that, in general, it is not possible to know a priori a specific value of k to correctly classify an unseen example.
publishDate 2003
dc.date.none.fl_str_mv 2003
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/39230
https://doi.org/10.1007/3-540-44862-4_83
url http://hdl.handle.net/11441/39230
https://doi.org/10.1007/3-540-44862-4_83
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computational Science — ICCS 2003, Lecture Notes in Computer Science, Volume 2658, pp 766-773 (2003)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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