Ensembles of EFuNNs: An Architecture for a Mutlimodule Classifier
نویسندگان
چکیده
This paper introduces an extension of the existing theory of the Evolving Fuzzy Neural Network (EFuNN) to also be a multi-module classifier. We call this proposed architecture multiEFuNN. The incorporation of the Evolving Clustering Method (ECM) 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 mult-module classifier is in the area of on-line learning and recall of data where there are a growing number of classes with more data coming. Preliminary results from experiments conducted using this architecture are compared to the existing single EFuNN classifier and reported.
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