Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for high-dimensional remote sensing data

For high-dimensional data, the appropriate selection of features has a significant effect on the cost and accuracy of an automated classifier. A feature selection technique using genetic algorithms is applied. For classification, hard and fuzzy kNN classifiers are compared. Composite Fuzzy classifier architectures are investigated. Experiments are conducted on AVIRIS data, and the results are evaluated in the paper.