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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

The selection of most relevant features can significantly reduce the resource requirement associated with fuzzy ARTMAP neural networks for the classification of high-dimensional data extracted from satellite imagery. This paper introduces a variant of a wrapper-type feature ranking technique that was previously proposed for ARTMAP neural networks by Parsons and Carpenter. As with the originally proposed technique, it evaluates the relevance of features based solely on between-class scatter from the geometry of internal ARTMAP categories. This paper also explores the inclusion into these feature ranking techniques of a within-class scatter measurement. Comparative simulations, performed on the Landsat multi-spectral imagery benchmark...

Abstract
I. Introduction
II. Feature Selection for Fuzzy ARTMAP Neural Networks
III. Between- and Within-Class Measures for Feature Ranking
IV. Experimental Methodology
V. Results and Discussion
VI. Conclusions
Acknowledgements
References
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