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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Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.291-296, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)
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Contributeur : Fabrice Rossi <>
Soumis le : jeudi 11 juin 2015 - 18:13:23
Dernière modification le : lundi 27 novembre 2017 - 14:14:02
Document(s) archivé(s) le : samedi 12 septembre 2015 - 11:11:18

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Distributed under a Creative Commons Paternité 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, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). 〈hal-01162981〉

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