Binary particle swarm optimization in classification

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dc.contributor.author Cervantes Rovira, Alejandro
dc.contributor.author Galván León, Inés María
dc.contributor.author Isasi Viñuela, Pedro
dc.date.accessioned 2009-06-16T11:17:47Z
dc.date.available 2009-06-16T11:17:47Z
dc.date.issued 2005
dc.identifier.bibliographicCitation Neural Network World, 2005, vol. 15, n.3, p. 229 - 241
dc.identifier.issn 1210-0552
dc.identifier.uri http://hdl.handle.net/10016/4424
dc.description.abstract Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of same well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), lo the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, lo compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Computer Science, Academy of Sciences of the Czech Republic
dc.rights © ICS AS CR
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Evolutionary computation
dc.subject.other Particle swarm optimization
dc.subject.other Pattern classification
dc.subject.other Swarm intelligence
dc.title Binary particle swarm optimization in classification
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 229
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 241
dc.identifier.publicationtitle Neural Network World
dc.identifier.publicationvolume 15
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