NAME

       pkfsann - feature selection for nn classifier

SYNOPSIS

       pkfsann -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.

       pkfsann  implements  a  number  of  feature selection techniques, among which a sequential
       floating forward search (SFFS).  Also consider the SVM classifier implemented in pksvm(1),
       which has been shown to be more robust to this type of problem than others.

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)

       --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)

       -a 0|1|2, --aggreg 0|1|2
              how  to combine aggregated classifiers, see also --rc option (0: no aggregation, 1:
              sum rule, 2: max rule).

       -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).

       -n number, --nneuron number
              number of neurons in hidden layers in neural network (multiple  hidden  layers  are
              set  by  defining  multiple  number of neurons: -nn 15 -nn 1, default is one hidden
              layer with 5 neurons)

       --connection 0|1
              connection rate (default: 1.0 for a fully connected network)

       -w weights, --weights weights
              weights for neural network.  Apply to fully connected network only,  starting  from
              first  input  neuron to last output neuron, including the bias neurons (last neuron
              in each but last layer)

       -l rate, --learning rate
              learning rate (default: 0.7)

       --maxit number
              number of maximum iterations (epoch) (default: 500)

SEE ALSO

       pksvm(1)

                                         24 January 2016                               pkfsann(1)