feature selection for artificial neural network classifier pkann

SYNOPSIS

Usage: 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.

The utility pkfsann implements a number of feature selection techniques, among which a sequential floating forward search (SFFS). Also consider the SVM classifier implemented in pksvm, which has been shown to be more robust to this type of problem than others.

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). 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	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.
  	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
  a	aggreg	unsigned short	0	how to combine aggregated classifiers, see also rc option (0: no aggregation, 1: sum rule, 2: max rule).
  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.
  n	nneuron	unsigned int	5	number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: -n 15 -n 1, default is one hidden layer with 5 neurons)
  	connection	float	1	connection reate (default: 1.0 for a fully connected network)
  w	weights	float	0	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	learning	float	0.7	learning rate (default: 0.7)
  	maxit	unsigned int	500	number of maximum iterations (epoch) (default: 500)

  Usage: pkfsann -t training -n number