Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity

  title={Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity},
  author={Patrick E. Meyer and Colas Schretter and Gianluca Bontempi},
  journal={IEEE Journal of Selected Topics in Signal Processing},
The paper presents an original filter approach for effective feature selection in microarray data characterized by a large number of input variables and a few samples. The approach is based on the use of a new information-theoretic selection, the double input symmetrical relevance (DISR), which relies on a measure of variable complementarity. This measure evaluates the additional information that a set of variables provides about the output with respect to the sum of each single variable… CONTINUE READING
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