%0 Conference Proceedings
%T Bayesian Smoothing of Decision Tree Soft Predictions and Evidential Evaluation
%+ Informatique, Image, Intelligence Artificielle (I3A)
%A Sutton-Charani, Nicolas
%< avec comité de lecture
%( Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part II. - ISBN 978-3-030-50143-3
%B IPMU 2020 - Information Processing and Management of Uncertainty in Knowledge-Based Systems
%C Lisbonne, Portugal
%V 2
%P 368-381
%8 2020-06-15
%D 2020
%R 10.1007/978-3-030-50143-3_28
%K Smoothing
%K Correction
%K Predictive probabilities
%K Decision trees
%K Bayesian empirical methods
%K Predictive belief functions
%K Uncertain evaluation
%Z Computer Science [cs]Conference papers
%X As for many classifiers, decision trees predictions are naturally probabilistic, with a frequentist probability distribution on labels associated to each leaf of the tree. Those probabilities have the major drawback of being potentially unreliable in the case where they have been estimated from a limited number of examples. Empirical Bayes methods enable the updating of observed probability distributions for which the parameters of the prior distribution are estimated from the data. This paper presents an approach of smoothing decision trees predictive binary probabilities with an empirical Bayes method. The update of probability distributions associated with tree leaves creates a correction concentrated on small-sized leaves, which improves the quality of probabilistic tree predictions. The amplitude of these corrections is used to generate predictive belief functions which are finally evaluated through the ensemblist extension of three evaluation indexes of predictive probabilities.
%G English
%L hal-02872165
%U https://imt-mines-ales.hal.science/hal-02872165
%~ INSTITUT-TELECOM
%~ EM-ALES
%~ UNIV-MONTPELLIER
%~ INSTITUTS-TELECOM
%~ EUROMOV-DHM