Abstract
Naïve Bayesian Tree is a high-accuracy classification method by combining decision tree and naïve Bayes together. It uses averaged global accuracy as the measurement of goodness in the induction process of the tree structure, and chooses the local classifier that is most specific for the target instance to make the decision. This paper mainly introduces a pruning strategy based on local accuracy estimation. Instead of directly using the most specific local classifier (mostly the classifier in a leaf node) to making classification in NBTree, our pruning strategy uses the measurement of local accuracy to guide the selection of local classifier for decision. Experimental results manifest that this pruning strategy is effective, especially for the NBTree with relatively more nodes.
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Xie, Z. (2006). Naïve Bayesian Tree Pruning by Local Accuracy Estimation. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_62
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DOI: https://doi.org/10.1007/11811305_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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