mRMR Feature Selection (using mutual information computation) 1.0

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.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Analysis,  Based,  Biologyvol,  Chris,  Criteria,  Framework,  Fuhui,  Intelligencevol,  Machine,  Maxdependency,  Maxrelevance,  Minredundancyquotieee,  Pattern,  Pp12261238,  Pp185205,  Quotfeature,  Systems,  Toohanchuan,  Transactions
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