A SOM combined with KNN for classification task

Silva L.A.; Del-Moral-Hernandez E.

A SOM combined with KNN for classification task

Tipo

Artigo de evento

Data de publicação

2011

Periódico

Proceedings of the International Joint Conference on Neural Networks

Citações (Scopus)

16

Autores

Silva L.A.
Del-Moral-Hernandez E.

Resumo

Classification 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.

Assuntos Scopus

ANOVA test , Artificial Neural Network , Automatic classification , Classification rates , Classification tasks , Individual classifiers , K-nearest neighbors , P-values , Statistical classifier