C4.5: programs for machine learning
C4.5: programs for machine learning
On the boosting ability of top-down decision tree learning algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Machine Learning
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Is random model better? On its accuracy and efficiency
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation Trees
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Parameter Estimation in Semi-Random Decision Tree Ensembling on Streaming Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Spectrum of variable-random trees
Journal of Artificial Intelligence Research
Random ensemble decision trees for learning concept-drifting data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
A Practical Differentially Private Random Decision Tree Classifier
Transactions on Data Privacy
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Random decision tree is an ensemble of decision trees. The feature at any node of a tree in the ensemble is chosen randomly from remaining features. A chosen discrete feature on a decision path cannot be chosen again. Continuous feature can be chosen multiple times, however, with a different splitting value each time. During classification, each tree outputs raw posterior probability. The probabilities from each tree in the ensemble are averaged as the final posterior probability estimate. Although remarkably simple and somehow counter-intuitive, random decision tree has been shown to be highly accurate under 0-1 loss and cost-sensitive loss functions. Preliminary explanation of its high accuracy is due to the "error-tolerance" property of probabilistic decision making. Our study has shown that the actual reason for random tree's superior performance is due to its optimal approximation to each example's true probability to be a member of a given class.