Properties of measures of information in evidence and possibility theories
Fuzzy Sets and Systems - Special Issue: Measures of Uncertainty
C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A non-specificity measure for convex sets of probability distributions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - special issue on models for imprecise probabilities and partial knowledge
On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments
Annals of Mathematics and Artificial Intelligence
Machine Learning
Inference for the Generalization Error
Machine Learning
Maximum of entropy for credal sets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Upper entropy of credal sets. Applications to credal classification
International Journal of Approximate Reasoning
An introduction to the imprecise Dirichlet model for multinomial data
International Journal of Approximate Reasoning
A Bayesian Random Split to Build Ensembles of Classification Trees
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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In this article, we shall present a method for combining classification trees obtained by a simple method from the imprecise Dirichlet model (IDM) and uncertainty measures on closed and convex sets of probability distributions, otherwise known as credal sets. Our combine method has principally two characteristics: it obtains a high percentage of correct classifications using a few number of classification trees and it can be parallelized to apply on very large databases.