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Proceedings Paper

Feature selection for neural network classifiers using saliency and genetic algorithms
Author(s): Edward E. DeRouin; Joe R. Brown; Guy Denney
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Paper Abstract

In this paper the authors present the results of a research investigation on feature selection methods for neural network classifiers. As problems presented to computers for analysis become more complex and data dimensionality grows in size, traditional methods of feature extraction are being taxed beyond the limits of their usefulness. New methods of feature selection show promise in the laboratory, but need to be proven with real-world solutions. The purpose of this research is to compare the performance of newly proposed methods of selecting features on three challenging problems using non- artificial data. A feature saliency technique, and several variants of genetic algorithms, and random feature selection are compared and contrasted.

Paper Details

Date Published: 25 March 1998
PDF: 10 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304822
Show Author Affiliations
Edward E. DeRouin, Intelligent Technologies Corp. (United States)
Joe R. Brown, Intelligent Technologies Corp. (United States)
Guy Denney, Intelligent Technologies Corp. (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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