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Article Dans Une Revue International Journal of Approximate Reasoning Année : 2016

Evidential calibration of binary SVM classifiers

Résumé

In machine learning problems, the availability of several classifiers trained on different data or features makes the combination of pattern classifiers of great interest. To combine distinct sources of information, it is necessary to represent the outputs of classifiers in a common space via a transformation called calibration. The most classical way is to use class membership probabilities. However, using a single probability measure may be insufficient to model the uncertainty induced by the calibration step, especially in the case of few training data. In this paper, we extend classical probabilistic calibration methods to the eviden-tial framework. Experimental results from the calibration of SVM classifiers show the interest of using belief functions in classification problems.
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Dates et versions

hal-01154794 , version 1 (23-05-2015)

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Philippe Xu, Franck Davoine, Hongbin Zha, Thierry Denoeux. Evidential calibration of binary SVM classifiers. International Journal of Approximate Reasoning, 2016, 72, pp.55-70. ⟨10.1016/j.ijar.2015.05.002⟩. ⟨hal-01154794⟩
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