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Training and evaluating classifiers from evidential data: application to E2M tree pruning

Abstract : In many application data are imperfect, imprecise or more generally uncertain. Many classification methods have been presented that can handle data in some parts of the learning or the inference process, yet seldom in the whole process. Also, most of the proposed approach still evaluate their results on precisely known data. However, there are no reason to assume the existence of such data in applications, hence the need for assessment method working for uncertain data. We propose such an approach here, and apply it to the pruning of E 2 M decision trees. This results in an approach that can handle data uncertainty wherever it is, be it in input or output variables, in training or in test samples.
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https://hal.archives-ouvertes.fr/hal-01923324
Contributor : Nicolas Sutton-Charani <>
Submitted on : Thursday, November 15, 2018 - 10:58:12 AM
Last modification on : Wednesday, June 24, 2020 - 4:18:15 PM
Long-term archiving on: : Saturday, February 16, 2019 - 1:29:15 PM

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  • HAL Id : hal-01923324, version 1

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Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux. Training and evaluating classifiers from evidential data: application to E2M tree pruning. Third International Conference on Belief Functions, BELIEF 2014, Sep 2014, Oxford, United Kingdom. ⟨hal-01923324⟩

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