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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Cadenas Figueredo, J.M. | - |
dc.contributor.author | Garrido Carrera, M.C. | - |
dc.contributor.author | Martínez España, R. | - |
dc.contributor.author | Muñoz, E. | - |
dc.contributor.author | Bonissone, P. | - |
dc.contributor.other | Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Ingeniería de la Información y las Comunicaciones | es |
dc.date.accessioned | 2024-01-28T09:12:53Z | - |
dc.date.available | 2024-01-28T09:12:53Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Soft Computing, 22(10). 2018 | es |
dc.identifier.issn | 1433-7479 | - |
dc.identifier.issn | 1432-7643 | - |
dc.identifier.uri | http://hdl.handle.net/10201/137866 | - |
dc.description | © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Soft Computing. To access the final edited and published work see https://doi.org/10.1007/s00500-017-2567-x | es |
dc.description.abstract | The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and realworld datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection. | es |
dc.format | application/pdf | es |
dc.format.extent | 20 | es |
dc.language | eng | es |
dc.publisher | Springer-Verlag Berlin Heidelberg | es |
dc.relation | This work is derived from the project TIN2014-52099-R funded by Ministerio de Economía y Competitividad of Spain. Type of project: National. | es |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | k-nearest neighbors | es |
dc.subject | Classification | es |
dc.subject | Imperfect Data | es |
dc.subject | Distance/Dissimilarity Measures | es |
dc.subject | Combination method | es |
dc.title | A Fuzzy k-Nearest Neighbors Classifier to Deal with Imperfect Data | es |
dc.type | info:eu-repo/semantics/article | es |
dc.identifier.doi | https://doi.org/10.1007/s00500-017-2567-x | - |
Aparece en las colecciones: | Artículos: Ingeniería de la Información y las Comunicaciones |
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