BOOTSTRAP_LNK(1)

NAME
bootstrap_lnk - Generate LNKnet Bootstrap Training and Test Data

SYNOPSIS
Create mutiple bootstrap sample training, test, and default files from one original LNKNet training and defaults file.

bootstrap_lnk [ -in_prefix in ] [ -out_prefix out ] [ -nreps n ] [ -seed n ]

EXAMPLE
bootstrap_lnk -in_prefix vowel -out_prefix boot -nreps 10 -seed 1

DESCRIPTION
Bootstrap sampling creates multiple training and test patterns from an original set of training patterns. New bootstrap training files are created by sampling the training patterns with replacement. Some training patterns will occur multiple times in the bootstrap training set and some will not occur in the bootstrap training set. Those that do not occur in the training set are put into the bootstrap test set. The bootstrap training, test, and defaults files can be used to create bagging committee classifiers [1], to estimate the generalization error of classifiers [2], or to estimate the variability in classifier outputs, decision regions, or ROC curves [2]. Bagging committee classifiers are created by training separate classifiers for each bootstrap training file and then combining the multiple classifiers as a committee using the LNKnet committee classifier to do the combining. Bootstrap samples can be used to estimate the variability in classifier outputs by training multiple classifiers using the separate bootstrap training files and examining the variability in decision regions, output, and ROC curves produced across the multiple classifiers for the same inputs. Finally, bootstrap samples can be used to extimate classification generalization errors by training multiple classifiers using bootstrap training files and combining the error rates observed on the corresponding test files and training files. For example, the ".632+" estimator described on page 220 of [2] combines the bootstrap error rate on the training files with the error rate on corresponding test files to produce a relatively unbiased estimate of classifier generalization error. in

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

-in_prefix name
    The prefix for the input training and defaults file. For example, if the input training file was named vowel.train and the input defaults file was named vowel.defaults, then the name would be vowel .

-out_prefix name
    The prefix for the output bootstrap training, test, and defaults file. For example, if the prefix was boot , and there were two repetitions, then the output files would be named boot0.train, boot0.test, boot0.defaults, boot1.train, boot1.test, boot1.defaults.

-nreps n
    The number of different bootstrap test files to create.

REFERENCES
1. Breiman, L., Bagging Predictors. Machine Learning, 26, 123-140, 1996.
2. Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. 2001, New York: Springer.

HISTORY
First written 21 Sept 1994 by Yuchen Lee Modified 1 Sept 2003 by Richard Lippmann

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