Abstract
Data fusion under the belief function framework has attracted the interest of many researchers over the past few years. Until now, many combination rules have been proposed in order to aggregate beliefs induced form dependent or independent information sources. Although the choice of the most appropriate rule among several alternatives is crucial, it still requires non-trivial effort. In this investigation, we suggest to evaluate and compare some combination rules when dealing with independent information sources in the context of the classifier fusion framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)
Deng, X., Deng, Y., Chan, F.T.S.: An improved operator of combination with adapted conflict. Ann. OR 223(1), 451–459 (2014)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Jousselme, A., Grenier, D., Bossé, E.: A new distance between two bodies of evidence. Inf. Fusion 2(2), 91–101 (2001)
Lefèvre, E., Elouedi, Z.: How to preserve the conflict as an alarm in the combination of belief functions? Decis. Support Syst. 56, 326–333 (2013)
Mandler, E.: Combining the classification results of independent classifiers based on the Dempster-Shafer theory of evidence. Pattern Recogn. Artif. Intell. 381–393 (1988)
Martin, A.: About conflict in the theory of belief functions. In: Belief Functions: Theory and Applications, pp. 161–168. Springer (2012)
Mercier, D., Cron, G., Denœux, T., Masson, M.: Fusion of multi-level decision systems using the transferable belief model. In: 7th International Conference on Information Fusion, FUSION’2005, vol. 2, pp. 655–658. IEEE (2005)
Murphy, P., Aha, D.: UCI repository databases. http://www.ics.uci.edu/mlearn (1996)
Ponti, M.: Combining classifiers: from the creation of ensembles to the decision fusion. In: 24th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pp. 1–10. IEEE (2011)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, New Jersey (1976)
Smets, P.: The application of the transferable belief model to diagnostic problems. Int. J. Intell. Syst. 13, 127–157 (1998)
Smets, P.: The transferable belief model for quantified belief representation. Handb. Defeasible Reason. Uncertain. Manage. Syst. 1, 267–301 (1998)
Vatsa, M., Singh, R., Noore, A., Singh, S.K.: Belief function theory based biometric match score fusion: case studies in multi-instance and multi-unit iris verification. In: 7th International Conference on Advances in Pattern Recognition (ICAPR), pp. 433–436 (2009)
Weiss, S.M., Kapouleas, I.: An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In: 11th International Joint Conference on Artificial Intelligence (IJCAI), pp. 781–787. Morgan Kaufmann (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Trabelsi, A., Elouedi, Z., Lefèvre, E. (2016). Belief Function Combination: Comparative Study Within the Classifier Fusion Framework. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-26690-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26688-6
Online ISBN: 978-3-319-26690-9
eBook Packages: Computer ScienceComputer Science (R0)