Name
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/AIPR.2004.41 | AIPR |
Keywords | DocType | ISBN |
particle swarm optimization,feature selection,pattern recognition,genetic algorithm,feature selection method,swarmed feature selection,dimensionality problem,important part,curse of dimensionality | Conference | 0-7695-2250-5 |
Citations | PageRank | References |
21 | 1.51 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiram A. Firpi | 1 | 41 | 3.14 |
| Erik Goodman | 2 | 145 | 15.19 |