pkfssvm - feature selection for nn classifier

       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 classifica‐
       tion 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 imple‐
       ments a number of feature selection techniques, among which a sequential floating forward search (SFFS).

       pkfssvm - feature selection for nn classifier

-tfilename, --trainingfilename
              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)

       -nnumber, --nfnumber
              number of features to select (0 to select optimal number, see also --ecost option)

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

       -vlevel, --verboselevel
              set to: 0 (results only), 1 (confusion matrix), 2 (debug)

       Advanced options

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

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

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

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

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

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

       -sbandband, --startbandband
              start band sequence number

       -ebandband, --endbandband
              end band sequence number

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

       -scalevalue, --scalevalue
              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)

       -svmttype, --svmtypetype
              type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

       -kttype, --kerneltypetype
              type of kernel function (linear,polynomial,radial,sigmoid)

       -kdvalue, --kdvalue
              degree in kernel function

       -gvalue, --gammavalue
              gamma in kernel function

       -c0value, --coef0value
              coef0 in kernel function

       -ccvalue, --ccostvalue
              the parameter C of C-SVC, epsilon-SVR, and nu-SVR

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

       -elossvalue, --elossvalue
              the epsilon in loss function of epsilon-SVR

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

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

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

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

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

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

       -cname, --classname
              list of class names.

       -rvalue, --reclassvalue
              list of class values (use same order as in --class option).

pkfsann(1)

                                                 01 January 2025                                      pkfssvm(1)

pkfssvm-ttraining-nnumber [options] [advancedoptions]