Learning Naive Bayes Tree for Conditional Probability Estimation

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ConferenceProceedings of the Canadian AI-2006 Conference, June 7-9, 2006., Québec, Québec, Canada
AbstractNaïve Bayes Tree uses decision tree as the general structure and deploys naïve Bayesian classifiers at leaves. The intuition is that naïve Bayesian classifiers work better than decision trees when the sample data set is small. Therefore, after several attribute splits when constructing a decision tree, it is better to use naïve Bayesian classifiers at the leaves than to continue splitting the attributes. In this paper, we propose a learning algorithm to improve the conditional probability estimation in the diagram of Naïve Bayes Tree. The motivation for this work is that, for cost-sensitive learning where costs are associated with conditional probabilities, the score function is optimized when the estimates of conditional probabilities are accurate. The additional benefit is that both the classification accuracy and Area Under the Curve (AUC) could be improved. On a large suite of benchmark sample sets, our experiments show that the CLL tree outperforms the state-of-art learning algorithms, such as Naïve Bayes Tree and naïve Bayes significantly in yielding accurate conditional probability estimation and improving classification accuracy and AUC.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48488
NPARC number5764957
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Record identifier5a784356-33bb-4f49-9ddc-79ca68bde2d8
Record created2009-03-29
Record modified2016-05-09
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