On the evolution of neural networks for pairwise classification using gene expression programming

Authors:
Stephen Johns;Marcus V. dos Santos
Affiliations:
Ryerson University, Toronto, ON, Canada;Ryerson University, Toronto, ON, Canada
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 2
Cited 0

Comparing Combination Rules of Pairwise Neural Networks Classifiers

Neural Processing Letters
Evolving accurate and compact classification rules with gene expression programming

IEEE Transactions on Evolutionary Computation

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Abstract

Neural networks are a common choice for solving classification problems, but require experimental adjustments of the topology, weights and thresholds to be effective. Success has been seen in the development of neural networks with evolutionary algorithms, making the extension of this work to classification problems a logical step. This paper presents the first known use of the Gene Expression Programming-based GEP-NN algorithm to design neural networks for classification purposes. The system uses pairwise decomposition to produce a series of binary classifiers for a given multi-class problem, with the results of the classifier set being combined by majority vote.