%FEATSELB Backward feature selection for classification% %  [W,R] = FEATSELB(A,CRIT,K,T,FID)%% INPUT	%   A     Dataset%   CRIT  String name of the criterion or untrained mapping %         (optional; default: 'NN', i.e. 1-Nearest Neighbor error)%   K     Number of features to select %         (optional; default: return optimally ordered set of all features)%   T     Tuning set (optional)%   N    Number of cross-validations%   FID   File ID to write progress to (default [], see PRPROGRESS)%% OUTPUT%   W     Output feature selection mapping%   R     Matrix with step-by-step results of the selection%% DESCRIPTION% Backward selection of K features using the dataset A. CRIT sets the % criterion used by the feature evaluation routine FEATEVAL. If the  % dataset T is given, it is used as test set for FEATEVAL. Alternatvely a% a number of cross-validation N may be supplied. For K = 0, the optimal % feature set (corresponding to the maximum value of FEATEVAL) is returned. % The result W can be used for selecting features by B*W. In this case, % features are ranked optimally. In R, the search is step by step reported:% % 	R(:,1) : number of features% 	R(:,2) : criterion value% 	R(:,3) : added / deleted feature%% SEE ALSO% MAPPINGS, DATASETS, FEATEVAL, FEATSELLR, FEATSEL,% FEATSELO, FEATSELF, FEATSELI, FEATSELP, FEATSELM, PRPROGRESS% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: featselb.m,v 1.10 2004/05/16 14:48:08 duin Exp $function [w,r] = featselb(a,crit,ksel,t,fid)	prtrace(mfilename);		if (nargin < 2) | isempty(crit)		prwarning(2,'No criterion specified, assuming 1-NN.');		crit = 'NN';	end	if (nargin < 3) | isempty(ksel)		ksel = 0; 		% Consider all the features and sort them.	end	if (nargin < 4)		prwarning(3,'No tuning set supplied.');		t = [];	end	if (nargin < 5)		fid = [];	end		if nargin == 0 | isempty(a)		% Create an empty mapping:		w = mapping(mfilename,{crit,ksel,t});	else		prprogress(fid,'\nfeatselb : Backward Feature Selection')		[w,r] = featsellr(a,crit,ksel,0,1,t,fid);		prprogress(fid,'featselb  finished\n')	end	w = setname(w,'Backward FeatSel');return