Cheat-sheetSDFEATSEL Feature selection PF=SDFEATSEL(DATA) [PF,RES]=SDFEATSEL(DATA,options) PF=SDFEATSEL(DATA,DEF) % Define feature subset manually Define feature subset by a trained decision tree PT PT=SDTREE(DATA) PF=SDFEATSEL(PT) Find features with zero variance PF=SDFEATSEL(DATA,'var==0') PF=SDFEATSEL(DATA,'var>0') % non-zero variance features INPUT DATA Input data set DEF Indices of features in DATA or cell array with names 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 'steps' Make N steps and return best subset (only forward and backward) '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 classifier 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. User may limit the number of steps performed by forward and backward searches with 'steps' option. Best solution is always returned. 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: