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On Learning Evidential Contextual Corrections from Soft Labels Using a Measure of Discrepancy Between Contour Functions

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)

Abstract

In this paper, a proposition is made to learn the parameters of evidential contextual correction mechanisms from a learning set composed of soft labelled data, that is data where the true class of each object is only partially known. The method consists in optimizing a measure of discrepancy between the values of the corrected contour function and the ground truth also represented by a contour function. The advantages of this method are illustrated by tests on synthetic and real data.

Keywords

Belief functions Contextual corrections Learning Soft labels 

Notes

Acknowledgement

The authors would like to thank the anonymous reviewers for their helpful and constructive comments, which have helped them to improve the quality of the paper and to consider new paths for future research.

Mrs. Mutmainah’s research is supported by the overseas 5000 Doctors program of Indonesian Religious Affairs Ministry (MORA French Scholarship).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.EA 3926 LGI2AUniv. ArtoisBéthuneFrance
  2. 2.UIN Sunan KalijagaYogyakartaIndonesia

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