In this work, we present a review of the state of the art of information-theoretic feature selection methods. The concepts of feature relevance, redundance, and complementarity (synergy) are clearly defined, as well as Markov blanket. The problem of optimal feature selection is defined. A unifying theoretical framework is described, which can retrofit successful heuristic criteria, indicating the approximations made by each method. A number of open problems in the field are presented. © 2013 Springer-Verlag London.
CITATION STYLE
Vergara, J. R., & Estévez, P. A. (2014, January). A review of feature selection methods based on mutual information. Neural Computing and Applications. https://doi.org/10.1007/s00521-013-1368-0
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