Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

Published 12 Jun 2015 in stat.ML and cs.LG | (1506.04177v1)

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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