Less is More - Genetic Optimisation of Nearest Neighbour Classifiers

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dc.creatorRamos, Vitorino
dc.creatorMuge, Fernando
dc.date2004-12-17
dc.date.accessioned2026-07-07T03:22:14Z
dc.date.available2026-07-07T03:22:14Z
dc.descriptionThe present paper deals with optimisation of Nearest Neighbour rule Classifiers via Genetic Algorithms. The methodology consists on implement a Genetic Algorithm capable of search the input feature space used by the NNR classifier. Results show that is adequate to perform feature reduction and simultaneous improve the Recognition Rate. Some practical examples prove that is possible to Recognise Portuguese Granites in 100%, with only 3 morphological features (from an original set of 117 features), which is well suited for real time applications. Moreover, the present method represents a robust strategy to understand the proper nature of the images treated, and their discriminant features. KEYWORDS: Feature Reduction, Genetic Algorithms, Nearest Neighbour Rule Classifiers (k-NNR).
dc.description9 pages, 7 figures, Author at http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_13.html
dc.identifierhttps://arxiv.org/abs/cs/0412070
dc.identifierhttp://arxiv.org/abs/cs/0412070
dc.identifierProc. RecPad 98 - 10th Portuguese Conference on Pattern Recognition, F.Muge, C.Pinto and M.Piedade Eds., ISBN 972-97711-0-3, pp. 293-301, Lisbon, March 1998
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32517
dc.subjectArtificial Intelligence
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.2; I.5
dc.titleLess is More - Genetic Optimisation of Nearest Neighbour Classifiers
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