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Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

Abstract : We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining a reasonable computational cost.
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https://hal.archives-ouvertes.fr/hal-01162981
Contributor : Fabrice Rossi <>
Submitted on : Thursday, June 11, 2015 - 6:13:23 PM
Last modification on : Sunday, January 19, 2020 - 6:38:32 PM
Long-term archiving on: : Saturday, September 12, 2015 - 11:11:18 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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  • HAL Id : hal-01162981, version 1
  • ARXIV : 1506.04177

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Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi. Search Strategies for Binary Feature Selection for a Naive Bayes Classifier. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.291-296. ⟨hal-01162981⟩

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