Swarmed Feature Selection

Authors:
Hiram A. Firpi;Erik Goodman
Affiliations:
Department of Electrical and Computer Engineering Michigan State University;Department of Electrical and Computer Engineering Michigan State University
Venue:
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Year:
2004

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Abstract

Feature selection is an important part of pattern recognition, helping to overcome the curse of dimensionality problem with classifiers, among other systems. In this work, we introduce a feature selection method using particle swarm optimization. Experiments using data of others and hyperspectral remote sensed data will be used to measure the performance of the algorithm. Its comparison with a genetic algorithm will be also shown.