Building nearest prototype classifiers using a Michigan approach PSO

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Mostrar el registro sencillo del ítem Cervantes Rovira, Alejandro Galván León, Inés María Isasi Viñuela, Pedro 2009-04-20T07:35:00Z 2009-04-20T07:35:00Z 2007-04
dc.identifier.bibliographicCitation IEEE Swarm Intelligence Symposium, 2007 : SIS 2007. p. 135-140
dc.identifier.isbn 1-4244-0708-7
dc.description IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007
dc.description.abstract This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.title Building nearest prototype classifiers using a Michigan approach PSO
dc.type conferenceObject
dc.type bookPart
dc.subject.eciencia Informática
dc.identifier.doi 10.1109/SIS.2007.368037
dc.rights.accessRights openAccess
dc.relation.eventdate 1-5 april 2007
dc.relation.eventplace Honolulu (Hawai, USA)
dc.relation.eventtitle IEEE Swarm Intelligence Symposium
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 135
dc.identifier.publicationlastpage 140
dc.identifier.publicationtitle IEEE Swarm Intelligence Symposium, 2007 : SIS 2007
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