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Communication Dans Un Congrès Année : 2018

Feature selection with Rényi min-entropy

Résumé

We consider the problem of feature selection, and we propose a new information-theoretic algorithm for ordering the features according to their relevance for classification. The novelty of our proposal consists in adopting Rényi min-entropy instead of the commonly used Shannon entropy. In particular, we adopt a notion of conditional min-entropy that has been recently proposed in the field of security and privacy, and that avoids the anomalies of previously-attempted definitions. This notion is strictly related to the Bayes error, which is a promising property for achieving accuracy in the classification. We evaluate our method on 2 classifiers and 3 datasets, and we show that it compares favorably with the corresponding one based on Shannon entropy.
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Dates et versions

hal-01830177 , version 1 (04-07-2018)
hal-01830177 , version 2 (16-08-2018)

Identifiants

  • HAL Id : hal-01830177 , version 2

Citer

Catuscia Palamidessi, Marco Romanelli. Feature selection with Rényi min-entropy. Artificial Neural Networks in Pattern Recognition - 8th IAPR TC3 Workshop (ANNPR 2018), Sep 2018, Siena, Italy. pp.226-239. ⟨hal-01830177v2⟩
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