  Recently, many improved naive Bayes methods have been developed with enhanced
discrimination capabilities. Among them, regularized naive Bayes (RNB) produces
excellent performance by balancing the discrimination power and generalization
capability. Data discretization is important in naive Bayes. By grouping
similar values into one interval, the data distribution could be better
estimated. However, existing methods including RNB often discretize the data
into too few intervals, which may result in a significant information loss. To
address this problem, we propose a semi-supervised adaptive discriminative
discretization framework for naive Bayes, which could better estimate the data
distribution by utilizing both labeled data and unlabeled data through
pseudo-labeling techniques. The proposed method also significantly reduces the
information loss during discretization by utilizing an adaptive discriminative
discretization scheme, and hence greatly improves the discrimination power of
classifiers. The proposed RNB+, i.e., regularized naive Bayes utilizing the
proposed discretization framework, is systematically evaluated on a wide range
of machine-learning datasets. It significantly and consistently outperforms
state-of-the-art NB classifiers.
