Cheat-sheetSDFEATSEL Feature selection PF=SDFEATSEL(DATA) [PF,RES]=SDFEATSEL(DATA,options) PF=SDFEATSEL(DATA,IND) % Define feature subset manually Define feature subset by a trained decision tree PT PT=SDTREE(DATA) PF=SDFEATSEL(DATA,PT) INPUT DATA Input data set IND Indices of features in DATA OUTPUT PF Feature selection pipeline RES Structure with detailed information on selection process OPTIONS 'method' Selection method (default: 'forward') 'individual' - Individual feature ranking 'forward' - Greedy forward search 'backward' - Greedy backward search 'floating' - Series of forward/backward searches 'rounds' - Number of floating rounds (default: 10) 'random' - Best solution from a set of randomly generated subsets 'count' - Number of random solutions (default: 200) 'bounds' - Vector [min,max] number of features taken randomly 'model',M Use error of untrained pipeline M as criterion 'from' Initial solution for forward, backward or floating search 'best' Return best N features (for forward or backward search only) 'test' External test set used for criteria evaluation 'trfrac' Fraction of DATA used for training (default: 0.75) 'nodisplay' Do not show any output DESCRIPTION SDFEATSEL selects a subset of features of the the data set DATA. By default SDFEATSEL minimizes the error of 1-NN classifier. Any untrained pipeline returning decisions may be supplied in 'model' option. By default, the forward greedy search is performed. The classifier is trained on 75% and tested on the rest of DATA. Floating search combines several rounds of full forward and backward search. By default it is initialized from a random search (use 'from' option to specify initial subset manually). Subsets found in floating search are returned in RES.feat cell array. READ MORE http://perclass.com/doc/guide/dimensionality_reduction.html#featsel
sdfeatsel is referenced in examples: