Abstract
In this paper, we propose Randomized Bayesian Network Classifiers (RBNC). It borrows the idea of ensemble learning by constructing a collection of semi-naive Bayesian network classifiers and then combines their predictions as the final output. Specifically, the structure learning of each component Bayesian network classifier is performed by just randomly choosing the parent of each attribute in addition to class attribute, and parameter learning is performed by using maximum likelihood method. RBNC retains many of naive Bayes’ desirable property, such as scaling linearly with respect to both the number of instances and attributes, needing a single pass through the training data and robust to noise, etc. On the 60 widely used benchmark UCI datasets, RBNC outperforms state-of-the-art Bayesian classifiers.
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Wang, Q., Li, P. (2013). Randomized Bayesian Network Classifiers. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_28
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DOI: https://doi.org/10.1007/978-3-642-38067-9_28
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