DocumentCode
2840124
Title
Swarmed feature selection
Author
Firpi, Hiram A. ; Goodman, Erik
Author_Institution
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
fYear
2004
fDate
13-15 Oct. 2004
Firstpage
112
Lastpage
118
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 are used to measure the performance of the algorithm. Its comparison with a genetic algorithm is also shown.
Keywords
optimisation; pattern classification; genetic algorithm; hyperspectral remote sensed data; particle swarm optimization; pattern recognition; swarmed feature selection; Birds; Equations; Genetic algorithms; Hyperspectral imaging; Hyperspectral sensors; Particle swarm optimization; Pattern recognition; Principal component analysis; Remote sensing; Space exploration;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory, 2004. ISIT 2004. Proceedings. International Symposium on
ISSN
1550-5219
Print_ISBN
0-7695-2250-5
Type
conf
DOI
10.1109/AIPR.2004.41
Filename
1409684
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