Using unreliable information sources generating conflicting evidence may lead to a large uncertainty, which significantly hurts the decision making process. Recently, many approaches have been taken to integrate conflicting data from multiple sources and/or fusing conflicting opinions from different entities. To explicitly deal with uncertainty, a belief model called Subjective Logic (SL), as a variant of Dumpster-Shafer Theory, has been proposed to represent subjective opinions and to merge multiple opinions by offering a rich volume of fusing operators, which have been used to solve many opinion inference problems in trust networks. However, the operators of SL are known to be lack of scalability in inferring unknown opinions from large network data as a result of the sequential procedures of merging multiple opinions. In addition, SL does not consider deriving opinions in the presence of conflicting evidence. In this work, we propose a hybrid inference method that combines SL and Probabilistic Soft Logic (PSL), namely, Collective Subjective Plus, CSL + , which is resistible to highly conflicting evidence or a lack of evidence. PSL can reason a belief in a collective manner to deal with large-scale network data, allowing high scalability based on relationships between opinions. However, PSL does not considermore »
Multidimensional Uncertainty-Aware Evidential Neural Networks
Traditional deep neural networks (NNs) have significantly
contributed to the state-of-the-art performance in the task of
classification under various application domains. However,
NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly
model the uncertainty of class probabilities and use them for
classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the
ENN is trained as a black box without explicitly considering
inherent uncertainty in data with their different root causes,
such as vacuity (i.e., uncertainty due to a lack of evidence) or
dissonance (i.e., uncertainty due to conflicting evidence). By
considering the multidimensional uncertainty, we proposed
a novel uncertainty-aware evidential NN called WGAN-ENN
(WENN) for solving an out-of-distribution (OOD) detection
problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain
class, more »
- Award ID(s):
- 1954409
- Publication Date:
- NSF-PAR ID:
- 10287533
- Journal Name:
- Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence
- Page Range or eLocation-ID:
- 7815 - 7822
- Sponsoring Org:
- National Science Foundation
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