feature selection for support vector machine classifier pksvm

SYNOPSIS

Usage: 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

• use either -short or --long options (both --long=value and --long value are supported)
• short option -h shows basic options only, long option --help shows all options

  short	long	type	default	description
  i	input	std::string		input test set (leave empty to perform a cross validation based on training only)
  t	training	std::string		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).
  n	nf	unsigned short	0	number of features to select (0 to select optimal number, see also ecost option)
  tln	tln	std::string		training layer name(s)
  label	label	std::string	label	identifier for class label in training vector file.
  bal	balance	unsigned int	0	balance the input data to this number of samples for each class
  random	random	bool	true	in case of balance, randomize input data
  min	min	int	0	if number of training pixels is less then min, do not take this class into account
  b	band	unsigned short		band index (starting from 0, either use band option or use start to end)
  sband	startband	unsigned short		Start band sequence number
  eband	endband	unsigned short		End band sequence number
  offset	offset	double	0	offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]
  scale	scale	double	0	scale value for each spectral band input features: refl=(DN[band]-offset[band])/scaleband
  svmt	svmtype	std::string	C_SVC	type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)
  kt	kerneltype	std::string	radial	type of kernel function (linear,polynomial,radial,sigmoid)
  kd	kd	unsigned short	3	degree in kernel function
  g	gamma	float	1	gamma in kernel function
  c0	coef0	float	0	coef0 in kernel function
  cc	ccost	float	1000	the parameter C of C-SVC, epsilon-SVR, and nu-SVR
  nu	nu	float	0.5	the parameter nu of nu-SVC, one-class SVM, and nu-SVR
  eloss	eloss	float	0.1	the epsilon in loss function of epsilon-SVR
  cache	cache	int	100	cache memory size in MB
  etol	etol	float	0.001	the tolerance of termination criterion
  shrink	shrink	bool	false	whether to use the shrinking heuristics
  pe	probest	bool	true	whether to train a SVC or SVR model for probability estimates
  sm	sm	std::string	sffs	feature selection method (sffs=sequential floating forward search,sfs=sequential forward search, sbs, sequential backward search ,bfs=brute force search)
  ecost	ecost	float	0.001	epsilon for stopping criterion in cost function to determine optimal number of features
  cv	cv	unsigned short	2	n-fold cross validation mode
  c	class	std::string		list of class names.
  r	reclass	short		list of class values (use same order as in classname opt.

  Usage: pkfssvm -t training -n number