Keep the Decision Tree and Estimate the Class Probabilities Using its Decision Boundary

@inproceedings{Alvarez2007KeepTD,
  title={Keep the Decision Tree and Estimate the Class Probabilities Using its Decision Boundary},
  author={Isabelle Alvarez and Stephan Bernard and Guillaume Deffuant},
  booktitle={IJCAI},
  year={2007}
}
This paper proposes a new method to estimate the class membership probability of the cases classified by a Decision Tree. This method provides smooth class probabilities estimate, without any modification of the tree, when the data are numerical. It applies a posteriori and doesn’t use additional training cases. It relies on the distance to the decision boundary induced by the decision tree. The distance is computed on the training sample. It is then used as an input for a very simple… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 18 references

Decision tree with better ranking Probabilistic outputs for support vector machines

  • Bartlett P. Schoelkopf B. Schurmans D. Smola
  • 2003

of the 14th European Conf

  • C. Flach P. Ferri, J. Hernandez. Improving the auc of probabilistic esti Proc
  • on Machine Learning., pages 121–132,
  • 2003
1 Excerpt

editor

  • A.J.J. Platt. Probabilistic outputs for support vector ma Smola
  • Advances in Large Margin Classifiers, pages 61–74…
  • 2000
1 Excerpt

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