Automatic finding of good classifiers following a biologically inspired metaphor

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Show simple item record Fernández, Fernando Isasi, Pedro 2009-11-30T12:02:33Z 2009-11-30T12:02:33Z 2002
dc.identifier.bibliographicCitation Computing and Informatics, 2002, vol. 21, n. 3, p. 205-220
dc.identifier.issn 1335-9150
dc.description.abstract The design of nearest neighbour classifiers can be seen as the partitioning of the whole domain in different regions that can be directly mapped to a class. The definition of the limits of these regions is the goal of any nearest neighbour based algorithm. These limits can be described by the location and class of a reduced set of prototypes and the nearest neighbour rule. The nearest neighbour rule can be defined by any distance metric, while the set of prototypes is the matter of design. To compute this set of prototypes, most of the algorithms in the literature require some crucial parameters as the number of prototypes to use, and a smoothing parameter. In this work, an evolutionary approach based on Nearest Neighbour Classifiers (ENNC) is introduced where no parameters are involved, thus overcoming all the problems derived from the use of the above mentioned parameters. The algorithm follows a biological metaphor where each prototype is identified with an animal, and the regions of the prototypes with the territory of the animals. These animals evolve in a competitive environment with a limited set of resources, emerging a population of animals able to survive in the environment, i.e. emerging a right set of prototypes for the above classification objectives. The approach has been tested using different domains, showing successful results, both in the classification accuracy and the distribution and number of the prototypes achieved.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Slovak Academy of Sciences, Institute of Informatics
dc.rights © Institute of Informatics
dc.subject.other Classifier design
dc.subject.other Nearest neighbour classifiers
dc.subject.other Evolutionary learning
dc.subject.other Biologically inspired algorithms
dc.title Automatic finding of good classifiers following a biologically inspired metaphor
dc.type article PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 205
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 220
dc.identifier.publicationtitle Computing and Informatics
dc.identifier.publicationvolume 21
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