Machine learningProbabilistic
Dempster-Shafer Fusion
Dempster-Shafer fusion is an ensemble method based on evidence theory (belief functions) that combines predictions from multiple sources by assigning basic probability masses to subsets of hypotheses. Rather than requiring a probability distribution over single outcomes, it allows uncertainty over sets of outcomes, providing a richer representation of confidence and doubt. Developed by Dempster (1968) and formalized by Shafer (1976), this method is particularly useful when sources are unreliable, conflicting, or provide partial evidence.
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Sources
- Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30(2), 205-247. DOI: 10.1111/j.2517-6161.1968.tb00722.x ↗
- Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press. link ↗