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Proceedings Paper

Designing neuroclassifier fusion system by immune genetic algorithm
Author(s): Jimin Liang; Heng Zhao; Wanhai Yang
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Paper Abstract

A multiple neural network classifier fusion system design method using immune genetic algorithm (IGA) is proposed. The IGA is modeled after the mechanics of human immunity. By using vaccination and immune selection in the evolution procedures, the IGA outperforms the traditional genetic algorithms in restraining the degenerate phenomenon and increasing the converging speed. The fusion system consists of N neural network classifiers that work independently and in parallel to classify a given input pattern. The classifiers' outputs are aggregated by a fusion scheme to decide the collective classification results. The goal of the system design is to obtain a fusion system with both good generalization and efficiency in space and time. Two kinds of measures, the accuracy of classification and the size of the neural networks, are used by IGA to evaluate the fusion system. The vaccines are abstracted by a self-adaptive scheme during the evolutionary process. A numerical experiment on the 'alternate labels' problem is implemented and the comparisons of IGA with traditional genetic algorithm are presented.

Paper Details

Date Published: 24 September 2001
PDF: 6 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441648
Show Author Affiliations
Jimin Liang, Xidian Univ. (China)
Heng Zhao, Xidian Univ. (China)
Wanhai Yang, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies

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