Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

TL;DR

This paper compares various feature selection methods for Naive Bayes classifiers dealing with large sets of redundant binary features, highlighting the effectiveness of wrapper approaches guided by error probability estimates.

Contribution

It demonstrates that wrapper methods guided by NBC error estimates outperform filter methods in selecting features for binary data with redundancy.

Findings

Wrapper approaches outperform filter methods in accuracy.

Wrapper methods maintain reasonable computational costs.

Guided by error probability estimates, wrapper methods are most effective.

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.