Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

Tsirizo Rabenoro (1, 2) , Jérôme Lacaille (1) , Marie Cottrell (2) , Fabrice Rossi (2)

1 SNECMA Villaroche [Moissy-Cramayel]
2 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

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.

Mots clés: Naive Bayes Classifier; Feature Selection; Wrapper; Foward Backward; MRMR; Mutual Information

Domaines: Machine Learning [stat.ML]

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⟩