A SOM-based classifier with enhanced structure learning
Christos Pateritsas, Minas Pertselakis, Andreas Stafylopatis · 2005
This work introduces an innovative synergistic model that aims to improve the efficiency of a neuro-fuzzy classifier, providing the means of online adaptation and fast learning. It combines the advantages of a self-organized map (SOM) network, as well as the benefits of a structure allocation fuzzy neural network. The system initializes its parameters using the clustering result on the SOM structure, while a novel approach of evaluating the input features leads to a more efficient way of handling the on-line learning rate of the training process. Experimental results on benchmark classification problems showed that this robust combination can also tackle tasks of great dimensionality in a successful manner.