Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Distribution theory and transform analysis: an introduction to generalized functions, with applications
On estimating probabilities in tree pruning
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self bounding learning algorithms
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Pessimistic decision tree pruning based Continuous-time
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
An Empirical Comparison of Pruning Methods for Ensemble Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
The Journal of Machine Learning Research
An analysis of reduced error pruning
Journal of Artificial Intelligence Research
Laplace's law of succession and universal encoding
IEEE Transactions on Information Theory
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
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Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone's Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5.