%FEATSELF Trainable mapping for forward feature selection % % [W,R] = FEATSELF(A,CRIT,K,T) % [W,R] = A*FEATSELF([],CRIT,K,T) % [W,R] = A*FEATSELF(CRIT,K,T) % [W,R] = FEATSELF(A,CRIT,K,N) % [W,R] = A*FEATSELF([],CRIT,K,N) % [W,R] = A*FEATSELF(CRIT,K,N) % % INPUT % A Training dataset % CRIT Name of the criterion or untrained mapping % (default: 'NN', i.e. the LOO 1-Nearest Neighbor error) % K Number of features to select (default: K = 0, return optimal set) % T Tuning dataset (optional) % N Number of cross-validations (optional) % % OUTPUT % W Output feature selection mapping % R Matrix with step-by-step results % % DESCRIPTION % Forward 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 using B*W. % The selected features are stored in W.DATA and can be found by +W. % In R, the search is reported step by step as: % % R(:,1) : number of features % R(:,2) : criterion value % R(:,3) : added / deleted feature % % SEE ALSO (PRTools Guide) % MAPPINGS, DATASETS, FEATEVAL, FEATSELLR, FEATSEL, % FEATSELO, FEATSELB, FEATSELI, FEATSELP, FEATSELM % Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl % Faculty of Applied Sciences, Delft University of Technology % P.O. Box 5046, 2600 GA Delft, The Netherlands % $Id: featself.m,v 1.3 2007/04/21 23:06:46 duin Exp $ function [w,r] = featself(varargin) varargin = shiftargin(varargin,{'char','prmapping'}); argin = setdefaults(varargin,[],'NN',0,[],[]); if mapping_task(argin,'definition') w = define_mapping(argin,'untrained','Forward FeatSel'); return end [a,crit,ksel,t,fid] = deal(argin{:}); [w,r] = featsellr(a,crit,ksel,1,0,t); return