Supervised feature selection by clustering using conditional mutual information-based distances

@article{Sotoca2010SupervisedFS,
  title={Supervised feature selection by clustering using conditional mutual information-based distances},
  author={Jos{\'e} Mart{\'i}nez Sotoca and Filiberto Pla},
  journal={Pattern Recognition},
  year={2010},
  volume={43},
  pages={2068-2081}
}
In this paper, a supervised feature selection approach is presented, which is based on metric applied on continuous and discrete data representations. This method builds a dissimilarity space using information theoretic measures, in particular conditional mutual information between features with respect to a relevant variable that represents the class labels. Applying a hierarchical clustering, the algorithm searches for a compression of the information contained in the original set of features… CONTINUE READING
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