Lazy Learning of Bayesian Rules
Machine Learning
SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Learning Lazy Naive Bayesian Classifiers for Ranking
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Enhancing SNNB with local accuracy estimation and ensemble techniques
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Boosting Local Naïve Bayesian Rules
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
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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.