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
Split Criterions for Variable Selection Using Decision Trees
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Review: Measures of divergence on credal sets
Fuzzy Sets and Systems
Upper entropy of credal sets. Applications to credal classification
International Journal of Approximate Reasoning
Measures of uncertainty for imprecise probabilities: An axiomatic approach
International Journal of Approximate Reasoning
Bayesian networks and the imprecise Dirichlet model applied to recognition problems
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Bagging schemes on the presence of class noise in classification
Expert Systems with Applications: An International Journal
Bagging decision trees on data sets with classification noise
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Expert Systems with Applications: An International Journal
Classification with decision trees from a nonparametric predictive inference perspective
Computational Statistics & Data Analysis
Analysis and extension of decision trees based on imprecise probabilities: Application on noisy data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Determining dependence relations using a new score based on imprecise probabilities
Intelligent Data Analysis
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In belief functions, there is a total measure of uncertainty that quantify the lack of knowledge and verifies a set of important properties. It is based on two measures: maximum of entropy and non-specificity. In this paper, we prove that the maximum of entropy verifies the same set of properties in a more general theory as credal sets and we present an algorithm that finds the probability distribution of maximum entropy for another interesting type of credal sets as probability intervals.