FEAST
=====
A FEAture Selection Toolbox for C/C++ & MATLAB/OCTAVE, v2.0.0.
FEAST provides implementations of common mutual information based filter
feature selection algorithms, and an implementation of RELIEF. All functions
expect discrete inputs (except RELIEF, which does not depend on the MIToolbox),
and they return the selected feature indices. These implementations were
developed to help our research into the similarities between these algorithms,
and our results are presented in the following paper:
```
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
G. Brown, A. Pocock, M.-J. Zhao, M. Lujan
Journal of Machine Learning Research, 13:27-66 (2012)
```
The weighted feature selection algorithms are described in:
```
Information Theoretic Feature Selection for Cost-Sensitive Problems
A. Pocock, N. Edakunni, M.-J. Zhao, M. Lujan, G. Brown.
ArXiv
```
If you use these implementations for academic research please cite the relevant paper
above. All FEAST code is licensed under the BSD 3-Clause License.
Contains implementations of:
mim, mrmr, mifs, cmim, jmi, disr, cife, icap, condred, cmi, relief, fcbf, betagamma
And weighted implementations of:
mim, cmim, jmi, disr, cmi
References for these algorithms are provided in the accompanying feast.bib file
(in BibTeX format).
FEAST works on discrete inputs, and all continuous values **must** be
discretised before use with FEAST. In our experiments we've found that using
10 equal width bins is suitable for many problems, though this is data set size
dependent. FEAST produces unreliable results when used with continuous inputs,
runs slowly and uses much more memory than usual. The discrete inputs should
have small cardinality, FEAST will treat values {1,10,100} the same way it
treats {1,2,3} and the latter will be both faster and use less memory.
MATLAB Example (using "data" as our feature matrix, and "labels" as the class label vector):
```
>> size(data)
ans =
(569,30) %% denoting 569 examples, and 30 features
```
```
>> selectedIndices = feast('jmi',5,data,labels) %% selecting the top 5 features using the jmi algorithm
selectedIndices =
28
21
8
27
23
```
```
>> selectedIndices = feast('mrmr',10,data,labels) %% selecting the top 10 features using the mrmr algorithm
selectedIndices =
28
24
22
8
27
21
29
4
7
25
```
```
>> selectedIndices = feast('mifs',5,data,labels,0.7) %% selecting the top 5 features using the mifs algorithm with beta = 0.7
selectedIndices =
28
24
22
20
29
```
The library is written in ANSI C for compatibility with the MATLAB mex
compiler, except for MIM, FCBF and RELIEF, which are written in MATLAB/OCTAVE
script. There is a different implementation of MIM available for use in the C
library.
MIToolbox v3.0.0 is required to compile these algorithms, and these
implementations supercede the example implementations given in that package
(they have more robust behaviour when used with unexpected inputs).
MIToolbox can be found at: http://www.github.com/Craigacp/MIToolbox/
The C library expects all matrices in column-major format (i.e. Fortran style).
This is for two reasons, a) MATLAB generates Fortran-style arrays, and b)
feature selection iterates over columns rather than rows, unlike most other ML
processes.
Compilation instructions:
- MATLAB/OCTAVE - run CompileFEAST.m
- Linux C shared library - use the included makefile
Update History
- 07/01/2017 - v2.0.0 - Added weighted feature selection, major refactoring of the code to improve speed and portability. FEAST functions now return the internal scores assigned by each criteria as well. Added a Java API via JNI. FEAST v2 is approximately 30% faster when called from Matlab.
- 12/03/2016 - v1.1.4 - Fixed an issue where Matlab would segfault if all features had zero MI with the label.
- 12/10/2014 - v1.1.2 - Updated documentation to note that FEAST expects column-major matrices.
- 11/06/2014 - v1.1.1 - Fixed an issue where MIM wasn't compiled into libFSToolbox.
- 22/02/2014 - v1.1.0 - Bug fixes in memory allocation, added a C implementation of MIM, moved the selected feature increment into the mex code.
- 12/02/2013 - v1.0.1 - Bug fix for 32-bit Windows MATLAB's lcc.
- 08/11/2011 - v1.0.0 - Public Release to complement the JMLR publication.
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包含以下实现: MIM, MRMR, MIFS, CMIM, JMI, DISR, CIFE, ICAP, CONDRED, CMI, RELIEF, FCBF, BETAGAMMA 以及以下各项的加权实现: MIM, CMIM, JMI, DISR, CMI
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FEAST-v2.0.0_1 (1).zip (87个子文件)
MIToolbox
include
MIToolbox
WeightedMutualInformation.h 3KB
MIToolbox.h 1KB
RenyiMutualInformation.h 2KB
RenyiEntropy.h 2KB
Entropy.h 3KB
MutualInformation.h 2KB
CalculateProbability.h 6KB
ArrayOperations.h 5KB
WeightedEntropy.h 3KB
Makefile 2KB
src
Entropy.c 4KB
RenyiMutualInformation.c 4KB
MutualInformation.c 5KB
WeightedMutualInformation.c 5KB
CalculateProbability.c 12KB
WeightedEntropy.c 5KB
RenyiEntropy.c 3KB
ArrayOperations.c 12KB
LICENSE 2KB
matlab
joint.m 614B
CompileMIToolbox.m 468B
MIToolboxMex.c 15KB
MIToolbox.m 3KB
cmi.m 906B
mi.m 557B
condh.m 746B
demonstration_algorithms
mRMR_D.m 2KB
mRMR_D_Mex.c 6KB
CMIM_Mex.c 5KB
CMIM.m 1KB
CompileDemos.m 410B
IAMB.m 1KB
DISR_Mex.c 6KB
DISR.m 2KB
WeightedMIToolboxMex.c 13KB
WeightedMIToolbox.m 3KB
RenyiMIToolbox.m 2KB
h.m 407B
RenyiMIToolboxMex.c 5KB
test
testMIToolbox.c 3KB
.gitignore 231B
README.md 4KB
FEAST
include
FEAST
FSAlgorithms.h 9KB
WeightedFSAlgorithms.h 7KB
FSToolbox.h 3KB
Makefile 4KB
src
WeightedCondMI.c 7KB
WeightedMIM.c 6KB
BetaGamma.c 8KB
JMI.c 7KB
CondMI.c 7KB
WeightedJMI.c 7KB
MIM.c 6KB
ICAP.c 8KB
CMIM.c 7KB
WeightedCMIM.c 7KB
WeightedDISR.c 8KB
DISR.c 8KB
mRMR_D.c 7KB
LICENSE 2KB
java
pom.xml 950B
src
native
FEASTJNIUtil.c 280B
FEASTJNI.c 9KB
craigacp_feast_WeightedFEAST.h 575B
FEASTJNIUtil.h 201B
WeightedFEASTJNI.c 7KB
craigacp_feast_FEAST.h 759B
main
java
craigacp
feast
FEAST.java 6KB
WeightedFEAST.java 5KB
Dataset.java 2KB
Test.java 13KB
FEASTUtil.java 4KB
ScoredFeatures.java 2KB
README.md 1KB
matlab
feast.m 4KB
FCBF.m 1KB
FSToolboxMex.c 14KB
testFSToolbox.m 3KB
WeightedFSToolboxMex.c 9KB
MIM.m 439B
WMIM.m 485B
weighted_feast.m 2KB
RELIEF.m 1KB
CompileFEAST.m 795B
.gitignore 221B
README.md 4KB
feast.bib 4KB
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