pktools  2.6.7
Processing Kernel for geospatial data

feature selection for support vector machine classifier pksvm


Usage: pkfssvm -t training -n number


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Classification problems dealing with high dimensional input data can be challenging due to the Hughes phenomenon. Hyperspectral data, for instance, can have hundreds of spectral bands and require special attention when being classified. In particular when limited training data are available, the classification of such data can be problematic without reducing the dimension.

The SVM classifier has been shown to be more robust to this type of problem than others. Nevertheless, classification accuracy can often be improved with feature selection methods. The utility pkfssvm implements a number of feature selection techniques, among which a sequential floating forward search (SFFS).