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The Tree Augmented Naive Bayes classifier is a type of probabilisticgraphical model that can represent some feature dependencies. In this work, wepropose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes(HRE-TAN) algorithm, which considers removing the hierarchical redundancyduring the classifier learning process, when coping with data containinghierarchically structured features. The experiments showed that HRE-TAN obtainssignificantly better predictive performance than the conventional TreeAugmented Naive Bayes classifier, and enhanced the robustness againstimbalanced class distributions, in aging-related gene datasets with GeneOntology terms used as features.

A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features