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Communication Dans Un Congrès Année : 2016

Co-training with Credal Models

Résumé

So-called credal classifiers offer an interesting approach when the reliability or robustness of predictions have to be guaranteed. Through the use of convex probability sets, they can select multiple classes as prediction when information is insufficient and predict a unique class only when the available information is rich enough. The goal of this paper is to explore whether this particular feature can be used advantageously in the setting of co-training, in which a classifier strengthen another one by feeding it with new labeled data. We propose several co-training strategies to exploit the potential indeterminacy of credal classifiers and test them on several UCI datasets. We then compare the best strategy to the standard co-training process to check its efficiency.
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Dates et versions

hal-01380427 , version 1 (13-10-2016)

Identifiants

Citer

Yann Soullard, Sébastien Destercke, Indira Mouttapa Thouvenin. Co-training with Credal Models. 7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2016), Sep 2016, Ulm, Germany. pp.92-104, ⟨10.1007/978-3-319-46182-3_8⟩. ⟨hal-01380427⟩
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