Online pattern classification with multiple neural network systems: an experimental study

Lim, Chee Peng and Harrison, Robert F. 2003, Online pattern classification with multiple neural network systems: an experimental study, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 33, no. 2, pp. 235-247.

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Title Online pattern classification with multiple neural network systems: an experimental study
Author(s) Lim, Chee Peng
Harrison, Robert F.
Journal name IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume number 33
Issue number 2
Start page 235
End page 247
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N. J
Publication date 2003-05
ISSN 1094-6977
1558-2442
Keyword(s) Adaptive resonance theory
Benchmark studies
Decision combination algorithms
Multiple neural network systems
Online learning
Summary In this paper, an empirical study of the development and application of a committee of neural networks on online pattern classification tasks is presented. A multiple classifier framework is designed by adopting an Adaptive Resonance Theory-based (ART) autonomously learning neural network as the building block. A number of algorithms for combining outputs from multiple neural classifiers are considered, and two benchmark data sets have been used to evaluate the applicability of the proposed system. Different learning strategies coupling offline and online learning approaches, as well as different input pattern representation schemes, including the "ensemble" and "modular" methods, have been examined experimentally. Benefits and shortcomings of each approach are systematically analyzed and discussed. The results are comparable, and in some cases superior, with those from other classification algorithms. The experiments demonstrate the potentials of the proposed multiple neural network systems in offering an alternative to handle online pattern classification tasks in possibly nonstationary environments.
Language eng
Field of Research 069999 Biological Sciences not elsewhere classified
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2003, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048784

Document type: Journal Article
Collection: Institute for Frontier Materials
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