Feature selection with missing data using mutual information estimators

@article{Doquire2012FeatureSW,
  title={Feature selection with missing data using mutual information estimators},
  author={Gauthier Doquire and Michel Verleysen},
  journal={Neurocomputing},
  year={2012},
  volume={90},
  pages={3-11}
}
Feature selection is an important preprocessing task for many machine learning and pattern recognition applications, including regression and classification. Missing data are encountered in many real-world problems and have to be considered in practice. This paper addresses the problem of feature selection in prediction problems where some occurrences of features are missing. To this end, the wellknown mutual information criterion is used. More precisely, it is shown how a recently introduced… CONTINUE READING
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