A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
This work addresses classification challenges in bioinformatics for researchers using Gene Ontology features, but it is incremental as it builds on an existing classifier.
The authors tackled the problem of classifying aging-related gene datasets with hierarchical Gene Ontology features by proposing the HRE-TAN algorithm, which removes hierarchical redundancy during learning, resulting in significantly better predictive performance and enhanced robustness against imbalanced classes compared to the conventional Tree Augmented Naive Bayes classifier.
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.