Improving the Performance of Boosting for Naive Bayesian Classification

  • Kai Ming Ting
  • Zijian Zheng
Conference paper

DOI: 10.1007/3-540-48912-6_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1574)
Cite this paper as:
Ting K.M., Zheng Z. (1999) Improving the Performance of Boosting for Naive Bayesian Classification. In: Zhong N., Zhou L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science, vol 1574. Springer, Berlin, Heidelberg

Abstract

This paper investigates boosting naive Bayesian classification. It first shows that boosting cannot improve the accuracy of the naive Bayesian classifier on average in a set of natural domains. By analyzing the reasons of boosting’s failures, we propose to introduce tree structures into naive Bayesian classification to improve the performance of boosting when working with naive Bayesian classification. The experimental results show that although introducing tree structures into naive Bayesian classification increases the average error of naive Bayesian classification for individual models, boosting naive Bayesian classifiers with tree structures can achieve significantly lower average error than the naive Bayesian classifier, providing a method of successfully applying the boosting technique to naive Bayesian classification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Kai Ming Ting
    • 1
  • Zijian Zheng
    • 1
  1. 1.School of Computing and MathematicsDeakin UniversityAustralia

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