Naïve Bayesian Tree Pruning by Local Accuracy Estimation
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.
KeywordsLeaf Node Child Node Local Accuracy Local Classifier Pruning Strategy
Unable to display preview. Download preview PDF.
- 1.Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. University of California, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
- 2.Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 3.Jiang, L., Guo, Y.: Learning Lazy Naive Bayesian Classifiers for Ranking. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, pp. 412–416. IEEE Computer Society, Los Alamitos (2005)Google Scholar
- 4.Kohavi, R.: Scaling up the accuracy of naïve-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the Second International Conference on Knowledge Discovery & Data Mining, pp. 202–207. AAAI Press/MIT press, Cambridge/Menlo Park (1996)Google Scholar