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Evidential calibration of binary SVM classifiers

International journal of approximate reasoning, 2016-05, Vol.72, p.55-70 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0888-613X ;EISSN: 1873-4731 ;DOI: 10.1016/j.ijar.2015.05.002

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  • Title:
    Evidential calibration of binary SVM classifiers
  • Author: Xu, Philippe ; Davoine, Franck ; Zha, Hongbin ; Denoeux, Thierry
  • Subjects: Computer Science ; Computer Vision and Pattern Recognition
  • Is Part Of: International journal of approximate reasoning, 2016-05, Vol.72, p.55-70
  • Description: 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.
  • Publisher: Elsevier
  • Language: English
  • Identifier: ISSN: 0888-613X
    EISSN: 1873-4731
    DOI: 10.1016/j.ijar.2015.05.002
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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