Splitting with confidence in decision trees with application to stream mining

@article{Rosa2015SplittingWC,
  title={Splitting with confidence in decision trees with application to stream mining},
  author={Rocco De Rosa and Nicol{\`o} Cesa-Bianchi},
  journal={2015 International Joint Conference on Neural Networks (IJCNN)},
  year={2015},
  pages={1-8}
}
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setting requires knowing when enough new information has been collected to justify splitting a leaf. Although some of the issues in the statistical analysis of Hoeffding trees have… CONTINUE READING

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