NC_CLASS_FEAT_SEL(1)

NAME
nc_class_feat_sel - Feature Selection using a Nearest Cluster Classifier and N-Fold Cross Validation

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

nc_class_feat_sel [ -classifier_options ]
[ -nc_class_options ] [ -kmeans_options ]
-cross_valid n [ -search_type n ] [ -stop_early ]
[ -search_n n ] [ -search_fparam file ]

EXAMPLE
nc_class_feat_sel -pathdata $LNKHOME/data/class \
-finput digit1.train -fdescribe digit1.defaults \
-npatterns 70 -cross_valid 5 -ncenters 4 -cluster_by_class \
-epochs 10 -nc_class_type 1

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 Nearest Cluster classifier and N-fold cross validation testing. nc_class_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

-nc_class_options
    These are the options listed on the nc_class_lnk(1) manual page.

-kmeans_options
    nc_class_feat_sel uses kmeans to create initial cluster centers for each nc_class 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.

SEE ALSO
classifier(1), nc_class_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)