Evolutionary design of nearest prototype classifiers

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dc.contributor.author Fernández, Fernando
dc.contributor.author Isasi, Pedro
dc.date.accessioned 2009-04-15T11:52:55Z
dc.date.available 2009-04-15T11:52:55Z
dc.date.issued 2004-07
dc.identifier.bibliographicCitation Journal of Heuristics, 2004, vol. 10, n. 4, p. 431-454
dc.identifier.issn 1572-9397 (Online)
dc.identifier.uri http://hdl.handle.net/10016/3977
dc.description.abstract In pattern classification problems, many works have been carried out with the aim of designing good classifiers from different perspectives. These works achieve very good results in many domains. However, in general they are very dependent on some crucial parameters involved in the design. These parameters have to be found by a trial and error process or by some automatic methods, like heuristic search and genetic algorithms, that strongly decrease the performance of the method. For instance, in nearest prototype approaches, main parameters are the number of prototypes to use, the initial set, and a smoothing parameter. In this work, an evolutionary approach based on Nearest Prototype Classifier (ENPC) is introduced where no parameters are involved, thus overcoming all the problems that classical methods have in tuning and searching for the appropiate values. The algorithm is based on the evolution of a set of prototypes that can execute several operators in order to increase their quality in a local sense, and with a high classification accuracy emerging for the whole classifier. This new approach has been tested using four different classical domains, including such artificial distributions as spiral and uniform distibuted data sets, the Iris Data Set and an application domain about diabetes. In all the cases, the experiments show successfull results, not only in the classification accuracy, but also in the number and distribution of the prototypes achieved.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer
dc.subject.other Classifier design
dc.subject.other Nearest prototype classifiers
dc.subject.other Evolutionary learning
dc.title Evolutionary design of nearest prototype classifiers
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1023/B:HEUR.0000034715.70386.5b
dc.subject.eciencia Informática
dc.identifier.doi 10.1023/B:HEUR.0000034715.70386.5b
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 431
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 454
dc.identifier.publicationtitle Journal of Heuristics
dc.identifier.publicationvolume 10
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