Abstract
Naive Bayes (NB) is a simple Bayesian classifier that assumes the conditional independence and augmented NB (ANB) models are extensions of NB by relaxing the independence assumption. The averaged one-dependence estimators (AODE) is a classifier that averages ODEs, which are ANB models. However, the expressiveness of AODE is still limited by the restricted structure of ODE. In this paper, we propose a model averaging method for NB Trees (NBTs) with flexible structures and present experimental results in terms of classification accuracy. Results of comparative experiments show that our proposed method outperforms AODE on classification accuracy.
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Kurokawa, M., Yokoyama, H., Sakurai, A. (2009). Averaged Naive Bayes Trees: A New Extension of AODE. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_16
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DOI: https://doi.org/10.1007/978-3-642-05224-8_16
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