Averaged naive bayes trees: A new extension of aode

Mori Kurokawa, Hiroyuki Yokoyama, Akito Sakurai

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルAdvances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
ページ191-205
ページ数15
DOI
出版ステータスPublished - 2009 12 1
イベント1st Asian Conference on Machine Learning, ACML 2009 - Nanjing, China
継続期間: 2009 11 22009 11 4

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5828 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other1st Asian Conference on Machine Learning, ACML 2009
CountryChina
CityNanjing
Period09/11/209/11/4

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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