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Minimum redundancy feature selection

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Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is usually described in its pairing with relevant feature selection as Minimum Redundancy Maximum Relevance (mRMR). This method was first proposed in 2003 by Hanchuan Peng and Chris Ding, followed by a theoretical formulation based on mutual information, along with the first definition of multivariate mutual information, published in IEEE Trans. Pattern Analysis and Machine Intelligence in 2005.

Minimum redundancy feature selection - Wikipedia Jump to content From Wikipedia, the free encyclopedia Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is usually described in its pairing with relevant feature selection as Minimum Redundancy Maximum Relevance (mRMR). This method was first proposed in 2003 by Hanchuan Peng and Chris Ding, [ 1 ] followed by a theoretical formulation based on mutual information, along with the first definition of multivariate

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