Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces

TL;DR

This paper introduces two new hierarchical Bayesian classifiers that prioritize positive feature values and eliminate redundancy, leading to improved predictive accuracy on bioinformatics datasets.

Contribution

The paper proposes two novel positive feature value prioritized HRE-TAN classifiers that enhance predictive performance by reducing hierarchical redundancy.

Findings

Better accuracy than conventional HRE-TAN

Effective on 28 bioinformatics datasets

Reduces hierarchical feature redundancy

Abstract

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.