Ensembles of EFuNNs: an architecture for a multimodule classifier

Author

Woodford, Brendon J. ; Kasabov, Nikola K.

Author_Institution

Dept. of Inf. Sci., Otago Univ., Dunedin, New Zealand

Volume

3

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

1573

Lastpage

1576

Abstract

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

Keywords

fuzzy neural nets; learning (artificial intelligence); pattern classification; real-time systems; evolving clustering; evolving fuzzy neural network; fuzzy output neurons; multiple module classifier; neural nets ensembles; online learning; pattern classification; Context modeling; Electrochemical machining; Fuzzy neural networks; Information science; Input variables; Learning systems; Neural networks; Neurons; Statistical analysis; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1008964

Filename

1008964