Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier. (25th July 2017)

• Record Type: Journal Article
• Authors: Moreira, Leandro Juvêncio; Silva, Leandro A.
• Other Names: Tanaka Toshihisa Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2017(2017)
• Publication Date: 2017-07-25
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2017/4263064
• Languages: English
• ISSNs: 1687-5265

Abstract

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