Self-organizing maps fusion: An approach to different size maps

Abstract

A set of neural networks working in an ensemble can lead to better classification results than just one neural network could. In the ensemble, the results of each neural network are fused resulting in a better generalization of the model. Kohonen Self-Organizing Maps is known as a method for dimensionality reduction, data visualization and also for data classification. This work presents a methodology to fuse different size Kohonen Self-Organizing Maps, with the objective of improving classification accuracy. A factorial experiment was conducted in order to test some variables influences. Computational simulations with some datasets from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite demonstrate an increase in the accuracy classification and the proposed method feasibility was evidenced by the Wilcoxon Signed Rank Test.