A neural network classifier based on Dempster-Shafer theory

By Thierry Denoeux. IEEE Trans. Syst. Man Cybern. Part A, 2000

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Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence (2008, background): “In the rest of this section, we will show that the above approach does allow to deﬁne a disjunctive counterpart of the cautious rule, provided it is based on a proper informational ordering of belief functions.”
Ensemble based systems in decision making (2006, methodology): “Several classifier fusion approaches have been proposed for this purpose, including combining classifiers using Dempster-Shafer based combination [78]–[80], ARTMAP [81], Learn++ [82], genetic algorithms [83] and other combinations of boosting/voting methods [84]–[86].”
The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties (2006, background): “Although the D–S theory was not originally proposed in relation to artificial intelligence (AI), it has found wide applications in AI and expert systems over the past two decades (Yager et al., 1994; Wallery, 1996; Anand et al., 1996; Benferhat et al., 2000; Denoeux, 1997, 2000a; Guan and Bell, 19”
Classification in the Presence of Label Noise: A Survey (2014, background): “However, several works have proposed heuristics to infer belief masses directly from data [78], [80], [271].”
A Light CNN for Deep Face Representation With Noisy Labels (2015, background): “The third category [6, 17] directly models the distribution of noisy label during learning.”