Variable selection for Na\"ive Bayes classification

The Na\"ive Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Na\"ive Bayes' assumption of conditional independence, and may deteriorate the method's performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method's execution.
In this paper we propose a sparse version of the Na\"ive Bayes classifier that …

analysis bayes classification cs.lg datasets features ive multivariate performance s performance stat.ml tractable