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)

                                                10 February 2020                                      pkfsann(1)