Provided by: pktools_2.6.7.3+ds-1_amd64 
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) 07 February 2018 pkfssvm(1)