RBF_FEAT_SEL(1)

NAME
rbf_feat_sel - Feature Selection using a Radial Basis Function Classifier and N-Fold Cross Validation

SYNOPSIS
Selects the best features from the given data set by building and testing a series of Radial Basis Function Classifiers. N classifiers are trained and tested for each feature list using N-fold cross validation.

rbf_feat_sel [ -classifier_options ] [ -rbf_options ]
[ -kmeans_options ] -cross_valid n [ -search_type n ]
[ -stop_early ] [ -search_n n ] [ -search_fparam file ] [ -roc_cost -roc_target n [-roc_lower x -roc_upper x ] ]

EXAMPLE
rbf_feat_sel -pathdata $LNKHOME/data/class \
-finput digit1.train -fdescribe digit1.defaults \
-npatterns 70 -cross_valid 5 -ncenters 4 -cluster_by_class

DESCRIPTION
Stores a list of features of a data base in order of their effectiveness in classification. The effectiveness of each tested feature list is found using a Radial Basis Function classifier and N-fold cross validation testing. rbf_feat_sel has three ways of conducting the search for the best features, a forward search, a backward search, and a forward-and-back search. These search methods are described on the feat_sel(1) manual page.

OPTIONS
-- Prints a list of all flags and arguments.

-classifier_options
    These are the options listed on the classifier(1) manual page. The -cross_valid flag must be set. The following options are ignored: -ferror, -fparam, -ninputs, -features, -cross_valid_from_file, -fcross_valid, -verbose, -verror

-rbf_options
    These are the options listed on the rbf_lnk(1) manual page.

-kmeans_options
    lvq_feat_sel uses kmeans to create initial cluster centers for each lvq model. The kmeans options are listed on the kmeans_lnk(1) manual page.

-search_fparam file
    Name of a file in pathexp for storing feature search results.

-search_verbosity n
    The value of n determines the quantity of search status messages printed during the feature search.

    0   Print out the feature list that got the best error rate and the list of all sorted features.

    1   Also print out the feature list found for each number of input features.

    2   Also print out the the results for each feature list tested.

-search_type n
    n determines the type of search done.

    0 Forward Search
        Build the feature list by selecting one feature to add each time.

    1 Backward Search
        Starting with all features, select features to remove one at a time.

    2 Forward and Back
        Combine a forward and a backward search. Add two features, one at a time, then take away one. Continue this way until all of the features have been added.

-stop_early
    Stop the feature search when search_n features has been reached. If searching forward, or using the forward-and-back search, stop when search_n features have been selected. If searching backward, search until only search_n features remain. In the parameter file, the whole list of features will be present. If this was a forward search, the features that remained after the first search_n were selected will be listed in random order. If this was a backward search, the features that remained after search_n features were removed will be listed in random order. In both cases, these "un-selected" features will be assigned an error rate of 100% in the parameter file.

-search_n n
    The number of features found when stopping the search early. This flag only has an effect when stop_early is selected.

-roc_cost
    Use the area under a Reciever Operatoring Characteristic plot as the search error rate, rather than the percent classification error. The ROC compares False alarm rate vs detection rate for varying thresholds through a single classifier output which has been normalized to sum to one. It is particularly useful in two class problems where class priors are very dissimilar, giving poor percent error results.

-roc_target
    The class who's outputs to use when generating the ROC curve.

-roc_lower -roc_upper
    Lower and limits of the false alarm range over which the ROC cost is calculated. This allows the user to compare particular regions in the ROC curves of classifiers, for example, where the false alarm rate is low.

SEE ALSO
classifier(1), rbf_lnk(1), kmeans_lnk(1), feat_sel(1)

HISTORY
First written 22 June 1994 by Linda Kukolich

BUGS
Send bug reports to Linda Kukolich (kukolich@sst.ll.mit.edu)