  The Tree Augmented Naive Bayes classifier is a type of probabilistic
graphical model that can represent some feature dependencies. In this work, we
propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes
(HRE-TAN) algorithm, which considers removing the hierarchical redundancy
during the classifier learning process, when coping with data containing
hierarchically structured features. The experiments showed that HRE-TAN obtains
significantly better predictive performance than the conventional Tree
Augmented Naive Bayes classifier, and enhanced the robustness against
imbalanced class distributions, in aging-related gene datasets with Gene
Ontology terms used as features.
