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NAME

       pkfssvm - feature selection for nn classifier

SYNOPSIS

       pkfssvm -t training -n number [options] [advanced options]

DESCRIPTION

       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).

OPTIONS

       -t filename, --training filename
              training vector file.  A single vector file contains all training features (must be
              set as: B0, B1, B2,...) for all classes (class numbers identified by label option).
              Use multiple training files for bootstrap aggregation (alternative to the  bag  and
              bsize options, where a random subset is taken from a single training file)

       -n number, --nf number
              number of features to select (0 to select optimal number, see also --ecost option)

       -i filename, --input filename
              input test set (leave empty to perform a cross validation based on training only)

       -v level, --verbose level
              set to: 0 (results only), 1 (confusion matrix), 2 (debug)

       Advanced options

       -tln layer, --tln layer
              training layer name(s)

       -label attribute, --label attribute
              identifier for class label in training vector file.  (default: label)

       -bal size, --balance size
              balance the input data to this number of samples for each class (default: 0)

       -random, --random
              in case of balance, randomize input data

       -min number, --min number
              if number of training pixels is less then min, do not take this class into account

       -b band, --band band
              band index (starting from 0, either use band option or use start to end)

       -sband band, --startband band
              start band sequence number

       -eband band, --endband band
              end band sequence number

       -offset value, --offset value
              offset      value      for      each      spectral     band     input     features:
              refl[band]=(DN[band]-offset[band])/scale[band]

       -scale value, --scale value
              scale     value     for      each      spectral      band      input      features:
              refl=(DN[band]-offset[band])/scale[band]  (use  0 if scale min and max in each band
              to -1.0 and 1.0)

       -svmt type, --svmtype type
              type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

       -kt type, --kerneltype type
              type of kernel function (linear,polynomial,radial,sigmoid)

       -kd value, --kd value
              degree in kernel function

       -g value, --gamma value
              gamma in kernel function

       -c0 value, --coef0 value
              coef0 in kernel function

       -cc value, --ccost value
              the parameter C of C-SVC, epsilon-SVR, and nu-SVR

       -nu value, --nu value
              the parameter nu of nu-SVC, one-class SVM, and nu-SVR

       -eloss value, --eloss value
              the epsilon in loss function of epsilon-SVR

       -cache number, --cache number
              cache memory size in MB (default: 100)

       -etol value, --etol value
              the tolerance of termination criterion (default: 0.001)

       -shrink, --shrink
              whether to use the shrinking heuristics

       -sm method, --sm method
              feature selection method (sffs=sequential floating forward  search,  sfs=sequential
              forward search, sbs, sequential backward search, bfs=brute force search)

       -ecost value, --ecost value
              epsilon  for  stopping  criterion  in  cost function to determine optimal number of
              features

       -cv value, --cv value
              n-fold cross validation mode (default: 0)

       -c name, --class name
              list of class names.

       -r value, --reclass value
              list of class values (use same order as in --class option).

SEE ALSO

       pkfsann(1)

                                         05 January 2019                               pkfssvm(1)