SPIDER The Spider Objects

Feature selection by mutual information

   A=MUTINF(C,H) returns a mutinf object initialized with hyperparameters H given a classifier C. 
   Performs feature selection by means of the mutual information between attributes and the target.
   If there is a given number of features to select, this number of features is selected according to
   the ranking based upon the mutatul information.
   If no number of features to select is given, a probalistic forward selection is used:
   A feature is selected, if P(I > epsilon) >= 95 , where I is the mutual information between the attribute
   and the target. (c.f. Zaffalon, Hutter, Robust Feature Selection by Mutual Information Distributions)
   Here epsilon is set to the mutual information that exists between a normally distributed random feature
   and the target.
   Hyperparameters, and their defaults   
    feat=[]		-- number of features to be selected 
    method='regression'  -- use feature selection for regression or classification
    c                    -- learning algorithm (e.g. svm)
    rank                 -- the ranking of the features
    child	        -- learning algorithm (e.g. svm)
    train, test, get_w 
    a=mutinf(svm); a.method='classification';a.feat=10; 
    [r,a]=train(a,toy); loss(test(a,toy))

Reference : Robust Feature Selection by Mutual Information Distributions
Author : Marco Zaalon and Marcus Hutter
Link : http://citeseer.ist.psu.edu/566806.html