Averaged naive bayes trees: A new extension of aode

Mori Kurokawa, Hiroyuki Yokoyama, Akito Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Naive Bayes (NB) is a simple Bayesian classifier that assumes the conditional independence and augmented NB (ANB) models are extensions of NB by relaxing the independence assumption. The averaged one-dependence estimators (AODE) is a classifier that averages ODEs, which are ANB models. However, the expressiveness of AODE is still limited by the restricted structure of ODE. In this paper, we propose a model averaging method for NB Trees (NBTs) with flexible structures and present experimental results in terms of classification accuracy. Results of comparative experiments show that our proposed method outperforms AODE on classification accuracy.

Original languageEnglish
Title of host publicationAdvances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
Number of pages15
Publication statusPublished - 2009 Dec 1
Event1st Asian Conference on Machine Learning, ACML 2009 - Nanjing, China
Duration: 2009 Nov 22009 Nov 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5828 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st Asian Conference on Machine Learning, ACML 2009


  • Augmented naive Bayes
  • Averaged one-dependence estimators
  • Model averaging
  • Naive Bayes
  • Naive Bayes trees

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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