Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier

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dc.contributor.authorMoreira L.J.
dc.contributor.authorSilva L.A.
dc.date.accessioned2024-03-13T00:50:58Z
dc.date.available2024-03-13T00:50:58Z
dc.date.issued2017
dc.description.abstract© 2017 Leandro Juvêncio Moreira and Leandro A. Silva.The k nearest neighbor is one of the most important and simple procedures for data classification task. The kNN, as it is called, requires only two parameters: the number of k and a similarity measure. However, the algorithm has some weaknesses that make it impossible to be used in real problems. Since the algorithm has no model, an exhaustive comparison of the object in classification analysis and all training dataset is necessary. Another weakness is the optimal choice of k parameter when the object analyzed is in an overlap region. To mitigate theses negative aspects, in this work, a hybrid algorithm is proposed which uses the Self-Organizing Maps (SOM) artificial neural network and a classifier that uses similarity measure based on information. Since SOM has the properties of vector quantization, it is used as a Prototype Generation approach to select a reduced training dataset for the classification approach based on the nearest neighbor rule with informativeness measure, named iNN. The SOMiNN combination was exhaustively experimented and the results show that the proposed approach presents important accuracy in databases where the border region does not have the object classes well defined.
dc.description.volume2017
dc.identifier.doi10.1155/2017/4263064
dc.identifier.issn1687-5273
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35848
dc.relation.ispartofComputational Intelligence and Neuroscience
dc.rightsAcesso Aberto
dc.titlePrototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier
dc.typeArtigo
local.scopus.citations5
local.scopus.eid2-s2.0-85027271471
local.scopus.subjectClassification analysis
local.scopus.subjectClassification approach
local.scopus.subjectData classification
local.scopus.subjectHybrid algorithms
local.scopus.subjectK-nearest neighbors
local.scopus.subjectNearest neighbor rule
local.scopus.subjectPrototype generations
local.scopus.subjectSimilarity measure
local.scopus.subjectAlgorithms
local.scopus.subjectCluster Analysis
local.scopus.subjectDatabases, Factual
local.scopus.subjectNeural Networks (Computer)
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85027271471&origin=inward
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