SPIDER The Spider Objects

RFE recursive feature elimination object

   A=RFE(C,[H]) returns an rfe object initialized with base algorithm
   C (usually an svm) and hyperparameters H. 
   RFE selects features via greedy backward selection: it trains a
   base algorithm, and calls the method get_w of the base algorithm
   and removes the feature(s) with the smallest absolute value returned
   from this method. get_w in a linear svm for example returns the
   weights for each dimension defining the hyperplane. Base algorithms
   with a method get_w include: one_vs_one, one_vs_rest, lda, stab, svm, svr
    Hyperparameters, and their defaults
     feat=[]        -- number of selected features to be selected, if feat=0, 
                       then the minimum number of feature will be computed.
     feature_group  -- Use this to supply a vector containing a group
           = []        index for each feature. Features with the same
                       group index are eliminated together. By default,
                       the vector 1:n is used, meaning features are
                       eliminated independently.
     force=[]       -- one value per feature or feature group. Features/groups
                       with negative values will be eliminated first (in
                       increasing order of force value). Features/groups
                       with positive values will be eliminated last (again,
                       in increasing force value order). A value of 0 means
                       that feature elimination order is determined by the
                       normal method.
     speed=0        -- rfe only removes a single feature each iteration 
                       if less than (speed) features left, otherwise it
                       halves the number of features each iteration.
     output_rank=0  -- whether a ranking is desired, if set to 0 then a
                       classification is perfomed after feature selection.
     child=svm      -- The base classifier to be used at each step
    a.rank=[]       -- ranking of features
    a.child=svm     -- base classifier trained at end of process
     train, test
   d=gen(toy); a=rfe; a.feat=10; a.output_rank=1;[r,a]=train(a,d);
   a.rank    - lists the chosen features in  order of importance

Reference : Gene selection for cancer classification using support vector machines
Author : I. Guyon, J. Weston, S. Barnhill, and V. Vapnik
Link : http://www.stanford.edu/class/cs374/CLUSTER_Guyon.pdf