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/*******************************************************************************
**
** 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
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