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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 4263064, 15 pages
Research Article

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

1Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Sao Paulo, SP, Brazil
2Computing and Informatics Faculty & Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Sao Paulo, SP, Brazil

Correspondence should be addressed to Leandro A. Silva; rb.eiznekcam@avlis.otsuguaordnael

Received 31 January 2017; Revised 13 June 2017; Accepted 15 June 2017; Published 25 July 2017

Academic Editor: Toshihisa Tanaka

Copyright © 2017 Leandro Juvêncio Moreira and Leandro A. Silva. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The nearest neighbor is one of the most important and simple procedures for data classification task. The , as it is called, requires only two parameters: the number of 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 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 NN. The SOMNN 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.