C4.5: programs for machine learning
C4.5: programs for machine learning
Fast discovery of association rules
Advances in knowledge discovery and data mining
Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Bayesian Averaging of Classifiers and the Overfitting Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mining optimal decision trees from itemset lattices
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting informative priors for Bayesian classification and regression trees
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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We present an algorithm for exact Bayes optimal classification from a hypothesis space of decision trees satisfying leaf constraints. Our contribution is that we reduce this classification problem to the problem of finding a rule-based classifier with appropriate weights. We show that these rules and weights can be computed in linear time from the output of a modified frequent itemset mining algorithm, which means that we can compute the classifier in practice, despite the exponential worst-case complexity. In experiments we compare the Bayes optimal predictions with those of the maximum a posteriori hypothesis.