image thumbnail

mRMR Feature Selection (using mutual information computation)

version 1.0.0.0 (523 KB) by Hanchuan Peng
This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection

21.4K Downloads

Updated 19 Apr 2007

No License

This package is the mRMR (minimum-redundancy maximum-relevancy) feature selection method in (Peng et al, 2005 and Ding & Peng, 2005, 2003), whose better performance over the conventional top-ranking method has been demonstrated on a number of data sets in recent publications. This version uses mutual information as a proxy for computing relevance and redundancy among variables (features). Other variations such as using correlation or F-test or distances can be easily implemented within this framework, too.

Hanchuan Peng, Fuhui Long, and Chris Ding, "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,"
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 27, No. 8, pp.1226-1238, 2005. [PDF]

Ding C., and Peng HC, "Minimum redundancy feature selection from microarray gene expression data," Journal of Bioinformatics and Computational Biology,
Vol. 3, No. 2, pp.185-205, 2005. [PDF]

Ding, C and Peng HC, Proc. 2nd IEEE Computational Systems Bioinformatics Conference (CSB 2003),
pp.523-528, Stanford, CA, Aug, 2003.

Cite As

Hanchuan Peng (2021). mRMR Feature Selection (using mutual information computation) (https://www.mathworks.com/matlabcentral/fileexchange/14608-mrmr-feature-selection-using-mutual-information-computation), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R14SP3
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!