Title :
Swarmed feature selection
Author :
Firpi, Hiram A. ; Goodman, Erik
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
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;
Conference_Titel :
Information Theory, 2004. ISIT 2004. Proceedings. International Symposium on
Print_ISBN :
0-7695-2250-5
DOI :
10.1109/AIPR.2004.41
