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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
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
Machine Learning
Maximum of entropy for credal sets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
A Semi-naive Bayes Classifier with Grouping of Cases
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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
International Journal of Approximate Reasoning
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
Constructing and evaluating alternative frames of discernment
International Journal of Approximate Reasoning
Imprecise probability in graphical models: achievements and challenges
ECSQARU'05 Proceedings of the 8th 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
Evaluating credal classifiers by utility-discounted predictive accuracy
International Journal of Approximate Reasoning
Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Classification with decision trees from a nonparametric predictive inference perspective
Computational Statistics & Data Analysis
Credal ensembles of classifiers
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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We present an application of the measure of entropy for credal sets: as a branching criterion for constructing classification trees based on imprecise probabilities which are determined with the imprecise Dirichlet model. We also justify the use of upper entropy as a global uncertainty measure for credal sets and present a deduction of this measure. We have carried out several experiments in which credal classification trees are built taking a global uncertainty measure as a basis. The results show how the introduced methodology improves the performance of traditional methods (Naive Bayes and C4.5), by providing a much lower error rate.