Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces
This work addresses classification challenges in bioinformatics, but it is incremental as it builds upon an existing hierarchical redundancy eliminated classifier.
The authors tackled the problem of improving classification in hierarchical feature spaces by proposing two new Bayesian classifiers that prioritize positive feature values, resulting in better predictive performance on 28 bioinformatics datasets compared to a conventional method.
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.