Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery

Authors:
Shixin Yu;Steve De Backer;Paul Scheunders
Affiliations:
VisionLab, Department of Physics, University of Antwerp, Groenenborgerlaan 171, Antwerp, B-2020, Belgium;VisionLab, Department of Physics, University of Antwerp, Groenenborgerlaan 171, Antwerp, B-2020, Belgium;VisionLab, Department of Physics, University of Antwerp, Groenenborgerlaan 171, Antwerp, B-2020, Belgium
Venue:
Pattern Recognition Letters
Year:
2002

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Abstract

For high-dimensional data, the appropriate selection of features has a significant effect on the cost and accuracy of an automated classifier. In this paper, a feature selection technique using genetic algorithms is applied. For classification, crisp and fuzzy k-nearest neighbor (kNN) classifiers are compared. Composite fuzzy classifier architectures are investigated. Experiments are conducted on airborne visible/infrared imaging spectrometer (AVIRIS) data, and the results are evaluated in the paper.