Ensembles of decision trees based on imprecise probabilities and uncertainty measures-Reference-Cited by-同舟云学术

Ensembles of decision trees based on imprecise probabilities and uncertainty measures

Published:2013-10 Issue:4 Volume:14 Page:423-430
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Abellán Joaquín

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Signal Processing,Software

Reference31 articles.

1. Y. Freund, R.E. Schapire, Experiments with a new boosting algorithm, in: Thirteenth International Conference on Machine Learning, San Francisco, 1996, pp. 148–156.

2. Bagging predictors;Breiman;Machine Learning,1996

3. Random forests;Breiman;Machine Learning,2001

4. Building classification trees using the total uncertainty criterion;Abellán;International Journal of Intelligent Systems,2003

5. Uncertainty and Information: Foundations of Generalized Information Theory;Klir,2006

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