Skip to main content
SpringerLink
Log in

An Efficient Computation of Dempster-Shafer Theory of Evidence Based on Native GPU Implementation

  • Conference paper
  • First Online:
Belief Functions: Theory and Applications (BELIEF 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12915))

Included in the following conference series:

Abstract

Interest in Dempster-Shafer’s theory of evidence has often run up against problems associated with the inherently exponential complexity of the calculation of the Belief and Plausibility measures. This can be mitigated by looking at the technological possibilities offered by GPU computing. Some preliminary attempts have been oriented towards parallelization of the computation, but none of them natively use the support offered by lower-level GPUs. In this paper, we introduce a set of Python functions for operations related to the Dempster-Shafer theory and outline its implementation based natively on GPU computing, highlighting the speedup possibilities in relation to a CPU-based or CPU-derived implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://numba.pydata.org.

  2. 2.

    https://pypi.org/project/pyds/.

References

  1. Barnett, J.A.: Computational methods for a mathematical theory of evidence. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, IJCAI’81, vol. 2, pp. 868–875. Morgan Kaufmann Publishers Inc., San Francisco (1981)

    Google Scholar 

  2. Benalla, M., Achchab, B., Hrimech, H.: On the computational complexity of Dempster’s rule of combination, a parallel computing approach. J. Comput. Sci. 50, 101283 (2021)

    Article  MathSciNet  Google Scholar 

  3. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)

    Article  MathSciNet  Google Scholar 

  4. Lal Prajapati, G., Saha, R.: Reeds: relevance and enhanced entropy based dempster Shafer approach for next word prediction using language model. J. Comput. Sci. 35, 1–11 (2019)

    Article  MathSciNet  Google Scholar 

  5. Orponen, P.: Dempster’s rule of combination is #p-complete. Artif. Intell. 44(1), 245–253 (1990)

    Article  MathSciNet  Google Scholar 

  6. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    Book  Google Scholar 

  7. Troiano, L., Díaz, I., Gaglione, C.: Matching media contents with user profiles by means of the Dempster-Shafer theory. In: 2017 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2017, Naples, Italy, 9–12 July 2017, pp. 1–6. IEEE (2017)

    Google Scholar 

  8. Troiano, L., Rodríuez-Muñiz, L.J., Díaz, I.: Discovering user preferences using Dempster-Shafer theory. Fuzzy Sets Syst. 278, 98–117 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Oviedo, Oviedo, Spain

    Noelia Rico & Irene Díaz

  2. Department of Innovation Systems, University of Salerno, Fisciano, Italy

    Luigi Troiano

Authors
  1. Noelia Rico
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luigi Troiano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Irene Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Noelia Rico .

Editor information

Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Thierry Denœux

  2. Université d'Artois, Béthune, France

    Eric Lefèvre

  3. Northwestern Polytechnical University, Xi'an, China

    Zhunga Liu

  4. Université d'Artois, Béthune, France

    Frédéric Pichon

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rico, N., Troiano, L., Díaz, I. (2021). An Efficient Computation of Dempster-Shafer Theory of Evidence Based on Native GPU Implementation. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88601-1_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88600-4

  • Online ISBN: 978-3-030-88601-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation