A SOM combined with KNN for classification task

dc.contributor.authorSilva L.A.
dc.contributor.authorDel-Moral-Hernandez E.
dc.date.accessioned2024-03-13T01:10:21Z
dc.date.available2024-03-13T01:10:21Z
dc.date.issued2011
dc.description.abstractClassification is a common task that humans perform when making a decision. Techniques of Artificial Neural Networks (ANN) or statistics are used to help in an automatic classification. This work addresses a method based in Self-Organizing Maps ANN (SOM) and K-Nearest Neighbor (KNN) statistical classifier, called SOM-KNN, applied to digits recognition in car plates. While being much faster than more traditional methods, the proposed SOM-KNN keeps competitive classification rates with respect to them. The experiments here presented contrast SOM-KNN with individual classifiers, SOM and KNN, and the results are classification rates of 89.485.6, 84.235.9 and 91.035.1 percent, respectively. The equivalency between SOM-KNN and KNN recognition results are confirmed with ANOVA test, which shows a p-value of 0.27. © 2011 IEEE.
dc.description.firstpage2368
dc.description.lastpage2373
dc.identifier.doi10.1109/IJCNN.2011.6033525
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36936
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.rightsAcesso Restrito
dc.titleA SOM combined with KNN for classification task
dc.typeArtigo de evento
local.scopus.citations16
local.scopus.eid2-s2.0-80054768903
local.scopus.subjectANOVA test
local.scopus.subjectArtificial Neural Network
local.scopus.subjectAutomatic classification
local.scopus.subjectClassification rates
local.scopus.subjectClassification tasks
local.scopus.subjectIndividual classifiers
local.scopus.subjectK-nearest neighbors
local.scopus.subjectP-values
local.scopus.subjectStatistical classifier
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80054768903&origin=inward
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