Scaling up the accuracy of averaged one-dependence estimators with decision tree-based attribute weighted

Jia Wu, Zhihua Cai, Zhechao Gao, Yaodong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

Averaged One-Dependence Estimators (AODE) is a most effective improved naive Bayes (NB) algorithm based on probabilistic classification learning technique. It addresses the attribute independence assumption of naive Bayes by averaging all of the dependence estimators. Researchers have proposed out many effective methods to improve the performance of AODE, such as attribute weighted method, backwards sequential elimination method, lazy elimination method and so on. In this paper, our research is focused on weighted method. We firstly present a simple filter method for setting attribute weights of AODE and then present an improved algorithm called Decision Tree-Based Attribute Weighted Averaged One-Dependence Estimator, simply DTWAODE. In DTWAODE, the weight for an attribute is set according to its depth in a decision tree which is built on the training samples. We experimentally tested DTWAODE in Weka system, using the whole 36 standard UCI data sets and the experimental results show that our new algorithm performs better than AODE.
Original languageEnglish
Title of host publicationIADIS European Conference on Data Mining 2010
Subtitle of host publicationpart of MCCSIS 2010
PublisherIADIS Press
Pages157-162
Number of pages6
ISBN (Electronic)9789728939236
Publication statusPublished - 2010
Externally publishedYes
EventIADIS International Conference on Data Mining 2010, part of the IADIS Multi Conference on Computer Science and Information Systems 2010, MCCSIS 2010 - Freiburg, Germany
Duration: 28 Jul 201031 Jul 2010

Conference

ConferenceIADIS International Conference on Data Mining 2010, part of the IADIS Multi Conference on Computer Science and Information Systems 2010, MCCSIS 2010
CountryGermany
CityFreiburg
Period28/07/1031/07/10

Keywords

  • Naive Bayes
  • AODE
  • Decision Tree
  • Attribute Weighted
  • Classification

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