Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity

By Patrick E. Meyer, Colas Schretter, Gianluca Bontempi. IEEE Journal on Selected Topics in Signal Processing, 2008

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A review of microarray datasets and applied feature selection methods (2014, methodology): “[81] introduced MASSIVE, a new information-theoretic filter approach for mining microarray data.”
Feature Selection (2016, background): “An alternative criterion, Joint Mutual Information (Yang and Moody 1999; Meyer et al. 2008) is proposed to increase the complementary information that is shared between unselected features and selected features given the class labels.”
A review of feature selection methods based on mutual information (2013, background): “(12) can be rewritten in the context of information theory as follows [43]:”
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection (2012, methodology): “The MIFS criterion focuses on reducing redundancy; an alternative approach was proposed by Yang and Moody (1999), and also later by Meyer et al. (2008) using the Joint Mutual Information (JMI), to focus on increasing complementary information between features.”