pkfssvm(1)

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

\FC-t\F[] filename, \FC--training\F[] 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)

\FC-n\F[] number, \FC--nf\F[] number
    number of features to select (0 to select optimal number, see also \FC--ecost\F[] option)

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

\FC-v\F[] level, \FC--verbose\F[] level
    set to: 0 (results only), 1 (confusion matrix), 2 (debug)

Advanced options

\FC-tln\F[] layer, \FC--tln\F[] layer
    training layer name(s)

\FC-label\F[] attribute, \FC--label\F[] attribute
    identifier for class label in training vector file. (default: label)

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

\FC-random\F[], \FC--random\F[]
    in case of balance, randomize input data

\FC-min\F[] number, \FC--min\F[] number
    if number of training pixels is less then min, do not take this class into account

\FC-b\F[] band, \FC--band\F[] band
    band index (starting from 0, either use band option or use start to end)

\FC-sband\F[] band, \FC--startband\F[] band
    start band sequence number

\FC-eband\F[] band, \FC--endband\F[] band
    end band sequence number

\FC-offset\F[] value, \FC--offset\F[] value
    offset value for each spectral band input features: \FCrefl[band]=(DN[band]-offset[band])/scale[band]\F[]

\FC-scale\F[] value, \FC--scale\F[] value
    scale value for each spectral band input features: \FCrefl=(DN[band]-offset[band])/scale[band]\F[] (use \FC0\F[] if scale min and max in each band to \FC-1.0\F[] and \FC1.0\F[])

\FC-svmt\F[] type, \FC--svmtype\F[] type
    type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

\FC-kt\F[] type, \FC--kerneltype\F[] type
    type of kernel function (linear,polynomial,radial,sigmoid)

\FC-kd\F[] value, \FC--kd\F[] value
    degree in kernel function

\FC-g\F[] value, \FC--gamma\F[] value
    gamma in kernel function

\FC-c0\F[] value, \FC--coef0\F[] value
    coef0 in kernel function

\FC-cc\F[] value, \FC--ccost\F[] value
    the parameter C of C-SVC, epsilon-SVR, and nu-SVR

\FC-nu\F[] value, \FC--nu\F[] value
    the parameter nu of nu-SVC, one-class SVM, and nu-SVR

\FC-eloss\F[] value, \FC--eloss\F[] value
    the epsilon in loss function of epsilon-SVR

\FC-cache\F[] number, \FC--cache\F[] number
    cache memory size in MB (default: 100)

\FC-etol\F[] value, \FC--etol\F[] value
    the tolerance of termination criterion (default: 0.001)

\FC-shrink\F[], \FC--shrink\F[]
    whether to use the shrinking heuristics

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

\FC-ecost\F[] value, \FC--ecost\F[] value
    epsilon for stopping criterion in cost function to determine optimal number of features

\FC-cv\F[] value, \FC--cv\F[] value
    n-fold cross validation mode (default: 0)

\FC-c\F[] name, \FC--class\F[] name
    list of class names.

\FC-r\F[] value, \FC--reclass\F[] value
    list of class values (use same order as in \FC--class\F[] option).

SEE ALSO
pkfsann(1)