Toward Optimal Feature Selection in Naive Bayes for Text Categorization

@article{Tang2016TowardOF,
  title={Toward Optimal Feature Selection in Naive Bayes for Text Categorization},
  author={Bo Tang and Steven Kay and Haibo He},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2016},
  volume={28},
  pages={2508-2521}
}
Automated feature selection is important for text categorization to reduce feature size and to speed up learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties… CONTINUE READING
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