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diff --git a/FEAST/FSToolbox/FSAlgorithms.h b/FEAST/FSToolbox/FSAlgorithms.h new file mode 100644 index 0000000..e6ddba8 --- /dev/null +++ b/FEAST/FSToolbox/FSAlgorithms.h @@ -0,0 +1,138 @@ +/******************************************************************************* +** +** FSAlgorithms.h +** Provides the function definitions for the list of algorithms implemented +** in the FSToolbox. +** +** Author: Adam Pocock +** Created: 27/06/2011 +** +** Copyright 2010/2011 Adam Pocock, The University Of Manchester +** www.cs.manchester.ac.uk +** +** Part of the FEAture Selection Toolbox (FEAST), please reference +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual +** Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +** +** +** Please check www.cs.manchester.ac.uk/~gbrown/fstoolbox for updates. +** +** Copyright (c) 2010-2011, A. Pocock, G. Brown, The University of Manchester +** All rights reserved. +** +** Redistribution and use in source and binary forms, with or without modification, +** are permitted provided that the following conditions are met: +** +** - Redistributions of source code must retain the above copyright notice, this +** list of conditions and the following disclaimer. +** - Redistributions in binary form must reproduce the above copyright notice, +** this list of conditions and the following disclaimer in the documentation +** and/or other materials provided with the distribution. +** - Neither the name of The University of Manchester nor the names of its +** contributors may be used to endorse or promote products derived from this +** software without specific prior written permission. +** +** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +** LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +** ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +** +*******************************************************************************/ + +/******************************************************************************* + * All algorithms take an integer k which determines how many features to + * select, the number of samples and the number of features. Additionally each + * algorithm takes pointers to the data matrix, and the label vector, and + * a pointer to the output vector. The output vector should be pre-allocated + * with sizeof(double)*k bytes. + * + * Some algorithms take additional parameters, which given at the end of the + * standard parameter list. + * + * Each algorithm uses a forward search, and selects the feature which has + * the maxmimum MI with the labels first. + * + * All the algorithms except CMIM use an optimised variant which caches the + * previously calculated MI values. This trades space for time, but can + * allocate large amounts of memory. CMIM uses the optimised implementation + * given in Fleuret (2004). + *****************************************************************************/ + +#ifndef __FSAlgorithms_H +#define __FSAlgorithms_H + +/******************************************************************************* +** mRMR_D() implements the minimum Relevance Maximum Redundancy criterion +** using the difference variant, from +** +** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy" +** H. Peng et al. IEEE Pattern Analysis and Machine Intelligence (PAMI) (2005) +*******************************************************************************/ +double* mRMR_D(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** CMIM() implements a discrete version of the +** Conditional Mutual Information Maximisation criterion, using the fast +** exact implementation from +** +** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation" +** F. Fleuret, JMLR (2004) +*******************************************************************************/ +double* CMIM(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** JMI() implements the JMI criterion from +** +** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data" +** H. Yang and J. Moody, NIPS (1999) +*******************************************************************************/ +double* JMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** DISR() implements the Double Input Symmetrical Relevance criterion +** from +** +** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification" +** P. Meyer and G. Bontempi, (2006) +*******************************************************************************/ +double* DISR(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** ICAP() implements the Interaction Capping criterion from +** +** "Machine Learning Based on Attribute Interactions" +** A. Jakulin, PhD Thesis (2005) +*******************************************************************************/ +double* ICAP(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** CondMI() implements the CMI criterion using a greedy forward search +*******************************************************************************/ +double* CondMI(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures); + +/******************************************************************************* +** betaGamma() implements the Beta-Gamma space from Brown (2009). +** This incoporates MIFS, CIFE, and CondRed. +** +** MIFS - "Using mutual information for selecting features in supervised neural net learning" +** R. Battiti, IEEE Transactions on Neural Networks, 1994 +** +** CIFE - "Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusion" +** D. Lin and X. Tang, European Conference on Computer Vision (2006) +** +** The Beta Gamma space is explained in our paper +** "Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection" +** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan +** Journal of Machine Learning Research (JMLR), 2011 +*******************************************************************************/ +double* BetaGamma(int k, int noOfSamples, int noOfFeatures, double *featureMatrix, double *classColumn, double *outputFeatures, double beta, double gamma); + +#endif |