Abstract
Naïve 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.
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Liang, H., Yan, Y. (2006). Learning Naïve Bayes Tree for Conditional Probability Estimation. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_39
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DOI: https://doi.org/10.1007/11766247_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34628-9
Online ISBN: 978-3-540-34630-2
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