Information-theoretic algorithm for feature selection

@article{Last2001InformationtheoreticAF,
  title={Information-theoretic algorithm for feature selection},
  author={Mark Last and Abraham Kandel and Oded Maimon},
  journal={Pattern Recognition Letters},
  year={2001},
  volume={22},
  pages={799-811}
}
Feature selection is used to improve efficiency of learning algorithms by finding an optimal subset of features. However, most feature selection techniques can handle only certain types of data. Additional limitations of existing methods include intensive computational requirements and inability to identify redundant variables. In this paper, we are presenting a novel, information-theoretic algorithm for feature selection, which finds an optimal set of attributes by removing both irrelevant and… CONTINUE READING
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