--- /dev/null
+-*- mode: text -*-
+
++----------------------------------------------------------------------+
+| This archive contains a simple implementation of the Conditional |
+| Mutual Information Maximization for feature selection. |
++----------------------------------------------------------------------+
+| Written by François Fleuret |
+| Contact <francois.fleuret@epfl.ch> for comments & bug reports |
+| Copyright (C) 2004 EPFL |
++----------------------------------------------------------------------+
+
+$Id: README,v 1.3 2007-08-23 08:36:50 fleuret Exp $
+
+0/ INTRODUCTION
+
+ The CMIM feature selection scheme is designed to select a small
+ number of binary features among a very large set in a context of two
+ class classification. It consists in picking features one after
+ another to maximize the conditional mutual information between the
+ selected feature and the class to predict given any one of the
+ features already picked. Such a criterion picks features which are
+ both individually informative yet pairwise weakly dependent. CMIM
+ stands for Conditional Mutual Information Maximization. See
+
+ Fast Binary Feature Selection with Conditional Mutual Information
+ Francois Fleuret
+ JMLR 5 (Nov): 1531--1555, 2004
+ http://www.jmlr.org/papers/volume5/fleuret04a/fleuret04a.pdf
+
+1/ INSTALLATION
+
+ To compile and test, just type 'make test'
+
+ This small test consists of generating a sample set for a toy
+ problem and testing CMIM, MIM and a random feature selection with
+ the naive Bayesian learner. The two populations of the toy problem
+ live in the [0, 1]^2 square. The positive population is in x^2+y^2 <
+ 1/4 and the negative population is everything else. Look at
+ create_samples.cc for more details. The features are responses of
+ linear classifiers generated at random.
+
+2/ DATA FILE FORMAT
+
+ Each data file, either for training or testing, starts with the
+ number of samples and the number of features. Then follow for every
+ single sample two lines, one with the value of the features (0/1)
+ and one with the value of the class to predict (0/1). Check the
+ train.dat and test.dat generated by create_samples to get an
+ example.
+
+ The test file has the same format, and the real class is used to
+ estimate the error rates. During test, the response of the naive
+ bayse before thresholding is saved in a result file (3rd parametre
+ of the --test option)
+
+3/ OPTIONS
+
+ --silent
+
+ Switch off all the outputs to stdout
+
+ --feature-selection <random|mim|cmim>
+
+ Selects the feature selection method
+
+ --classifier <bayesian|perceptron>
+
+ Selects the classifier type
+
+ --error <standard|ber>
+
+ Choses which error to minimize during bias estimation for the CMIM
+ + naive Bayesian.
+
+ standard = P(f(X) = 0, Y = 1) + P(f(X) = 1, Y = 0)
+
+ ber = (P(f(X) = 0 | Y = 1) + P(f(X) = 1 | Y = 0))/2
+
+ --nb-features <int: nb of features>
+
+ Selects the number of selected features
+
+ --cross-validation <file: data set> <int: nb test samples> <int: nb loops>
+
+ Do cross-validation
+
+ --train <file: data set> <file: classifier>
+
+ Build a classifier and save it on disk
+
+ --test <file: classifier> <file: data set> <file: result>
+
+ Load a classifier and test it on a dataset
+
+4/ LICENCE
+
+ This program is free software; you can redistribute it and/or modify
+ it under the terms of the GNU General Public License version 3 as
+ published by the Free Software Foundation.
+
+ This program is distributed in the hope that it will be useful, but
+ WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ General Public License for more details.
projects / cmim.git / blobdiff