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A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force

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A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force

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dc.contributor.author Aguilera, Juan es_ES
dc.contributor.author González, Luis C. es_ES
dc.contributor.author Montes-y-Gómez, Manuel es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2021-01-15T04:31:10Z
dc.date.available 2021-01-15T04:31:10Z
dc.date.issued 2019 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/159134
dc.description.abstract [EN] The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper we propose a weighting based on the Newton's gravitational force, so that a mass (or relevance) has to be assigned to each instance. We evaluated this idea in the kNN context over 13 benchmark data sets used for binary and multi-class classification experiments. Results in F1 score, statistically validated, suggest that our proposal outperforms the original version of kNN and is statistically competitive with the distance weighted kNN version as well. es_ES
dc.description.sponsorship This research was partially supported by CONACYT-Mexico (project FC-2410). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.relation.ispartof Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject KNN es_ES
dc.subject Newton's gravitational force es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-13469-3_36 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONACyT//FC 2016-2410/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-71147-C2-1-P/ES/COMPRENSION DEL LENGUAJE EN LOS MEDIOS DE COMUNICACION SOCIAL - REPRESENTANDO CONTEXTOS DE FORMA CONTINUA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Aguilera, J.; González, LC.; Montes-Y-Gómez, M.; Rosso, P. (2019). A New Weighted k-Nearest Neighbor Algorithm Based on Newton¿s Gravitational Force. Lecture Notes in Computer Science. 11401:305-313. https://doi.org/10.1007/978-3-030-13469-3_36 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 23rd Iberoamerican Congress on Pattern Recognition (CIARP 2018) es_ES
dc.relation.conferencedate Noviembre 19-22,2018 es_ES
dc.relation.conferenceplace Madrid, Spain es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-13469-3_36 es_ES
dc.description.upvformatpinicio 305 es_ES
dc.description.upvformatpfin 313 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11401 es_ES
dc.relation.pasarela S\409389 es_ES
dc.contributor.funder Consejo Nacional de Ciencia y Tecnología, México es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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