Uniqueness of information measure in the theory of evidence
Fuzzy Sets and Systems - Special Issue: Measures of Uncertainty
Artificial Intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Decision making on the sole basis of statistical likelihood
Artificial Intelligence
Semi-supervised learning with an imperfect supervisor
Knowledge and Information Systems
A Boosting Approach to remove Class Label Noise
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Pruning belief decision tree methods in averaging and conjunctive approaches
International Journal of Approximate Reasoning
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
Journal of Classification
International Journal of Approximate Reasoning
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
Belief functions on real numbers
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Partially supervised learning by a credal EM approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning from ambiguously labeled examples
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Target identification based on the transferable belief model interpretation of dempster-shafer model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Extending stochastic ordering to belief functions on the real line
Information Sciences: an International Journal
Learning from data with uncertain labels by boosting credal classifiers
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Expert Systems with Applications: An International Journal
Maximum likelihood estimation from fuzzy data using the EM algorithm
Fuzzy Sets and Systems
Nonparametric criteria for supervised classification of fuzzy data
International Journal of Approximate Reasoning
Random subspace evidence classifier
Neurocomputing
Estimating mutual information for feature selection in the presence of label noise
Computational Statistics & Data Analysis
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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This paper addresses classification problems in which the class membership of training data are only partially known. Each learning sample is assumed to consist of a feature vector x"i@?X and an imprecise and/or uncertain ''soft'' label m"i defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here, it is assumed that the feature vectors are generated from a mixture model. Using the generalized Bayesian theorem, an extension of Bayes' theorem in the belief function framework, we derive a criterion generalizing the likelihood function. A variant of the expectation maximization (EM) algorithm, dedicated to the optimization of this criterion is proposed, allowing us to compute estimates of model parameters. Experimental results demonstrate the ability of this approach to exploit partial information about class labels.