The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
Boosting a weak learning algorithm by majority
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Pairwise classifier combination using belief functions
Pattern Recognition Letters
Building ensemble classifiers using belief functions and OWA operators
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A definition of subjective possibility
International Journal of Approximate Reasoning
The combination of multiple classifiers using an evidential reasoning approach
Artificial Intelligence
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
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In this article, we investigate supervised learning when training data are associated with uncertain labels. We tackle this problem within the theory of belief functions. Each training pattern xi is thus associated with a basic belief assignment, representing partial knowledge of its actual class. Here, we propose to use the approach known as boosting to solve the classification problem. We propose a variant of the AdaBoost algorithm where the outputs of the classifiers are interpreted as belief functions. During training, our algorithm estimates the reliability of each classifier to identify patterns from the various classes. During test phase, the outputs of the classifiers are first discounted according to these reliabilities, and then combined using a suitable rule. Experiments conducted on classical datasets show that our algorithm is comparable to AdaBoost in accuracy. Processing EEG data with imperfect labels clearly demonstrates the interest of taking into account the reliability of the labelling, and thus the relevance of our approach.