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

Tipo
Artigo
Data de publicação
2017
Periódico
Computational Intelligence and Neuroscience
Citações (Scopus)
5
Autores
Moreira L.J.
Silva L.A.
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Título de Volume
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Resumo
© 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.
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Assuntos Scopus
Classification analysis , Classification approach , Data classification , Hybrid algorithms , K-nearest neighbors , Nearest neighbor rule , Prototype generations , Similarity measure , Algorithms , Cluster Analysis , Databases, Factual , Neural Networks (Computer)
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