Ensembles of EFuNNs: an architecture for a multimodule classifier

This paper introduces an extension to the existing theory of the evolving fuzzy neural network (EFuNN) for it to be a multi-module classifier as well. We call this proposed architecture multi-EFuNN. The incorporation of the evolving clustering method is used to partition the input space of the dataset and also determine how many EFuNNs are to be used to classify it. The main advantages of this multi-module classifier is in the areas of online learning and recall of data where there are a growing number of classes with more data coming. Preliminary results conducted using this architecture are compared to the existing single EFuNN classifier and reported.