Improving the Performance of Boosting for Naive Bayesian Classification

Authors:
Kai Ming Ting;Zijian Zheng
Affiliations:
-;-
Venue:
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Year:
1999

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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.