  The Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN)
classifier is a semi-naive Bayesian model that learns a type of hierarchical
redundancy-free tree-like feature representation to estimate the data
distribution. In this work, we propose two new types of positive feature values
prioritized hierarchical redundancy eliminated tree augmented naive Bayes
classifiers that focus on features bearing positive instance values. The two
newly proposed methods are applied to 28 real-world bioinformatics datasets
showing better predictive performance than the conventional HRE-TAN classifier.
