Averaged Extended Tree Augmented Naive Classifier

Aaron Meehan,Cassio de Campos

doi:10.3390/e17075085

Abstract

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Highlights

Bayesian network classifiers are based on learning the relations of independence among variables in a domain in order to predict the label of a targeted variable
Analysis [41,42]), for which we developed an extension with the Extended Tree Augmented Naive Bayes (ETAN) and Averaged Extended Tree Augmented Naive Bayes (AETAN) methods
We use the Bayesian Dirichlet equivalent uniform (BDeu) score with hyper-parameter α∗ = 5 unless specified

Summary

Introduction

Bayesian network classifiers are based on learning the relations of (conditional) independence among variables ( called attributes) in a domain in order to predict the label (or state) of a targeted variable ( called class) They have been shown to perform well with respect to other general purpose classifiers [1,2]. TAN on the other hand weakens this assumption of independence by using a tree structure wherein each attribute directly depends on the class and one other attribute Both of these classifiers are based on the Bayesian networks [3,4].

Background

Accuracy Measures

Averaged ETAN

Experiments

Conclusions