Skip to Main content Skip to Navigation
Conference papers

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

Isabelle Alvarez 1 Stephan Bernard Guillaume Deffuant
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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 one-dimension kernel-based density estimator, which provides an estimate of the class membership probability. This geometric method gives good results even with pruned trees, so the intelligibility of the tree is fully preserved.
Document type :
Conference papers
Complete list of metadata
Contributor : Lip6 Publications Connect in order to contact the contributor
Submitted on : Tuesday, June 21, 2016 - 3:59:21 PM
Last modification on : Tuesday, October 12, 2021 - 2:18:02 PM


  • HAL Id : hal-01335029, version 1
  • IRSTEA : PUB00020610


Isabelle Alvarez, Stephan Bernard, Guillaume Deffuant. Keep the Decision Tree and Estimate the Class Probabilities using its Decision Boundary. The 20th International Joint Conference on Artificial Intelligence 2007, Jan 2007, Hyderabad, India. pp.654-659. ⟨hal-01335029⟩



Record views