Two source code files of 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.
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.
Ding, C and Peng HC, Proc. 2nd IEEE Computational Systems Bioinformatics Conference (CSB 2003),
pp.523-528, Stanford, CA, Aug, 2003.
** Note that you need to download the mutual information computing toolbox of the same author. ***
Hanchuan Peng (2021). minimum-redundancy maximum-relevance feature selection (https://www.mathworks.com/matlabcentral/fileexchange/14916-minimum-redundancy-maximum-relevance-feature-selection), MATLAB Central File Exchange. Retrieved .
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