Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks

• URL: http://arxiv.org/abs/2509.13338v1
• Date: Thu, 11 Sep 2025 13:12:22 GMT
• Title: Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks
• Authors: Hassan Gharoun, Mohammad Sadegh Khorshidi, Kasra Ranjbarigderi, Fang Chen, Amir H. Gandomi,
• Abstract summary: This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making.<n>For each test instance, exemplars are retrieved in an embedding space; their predictive distributions are fused via Dempster-Shafer theory.<n>Because the supporting evidences are explicit, decisions are transparent and auditable.
• Score: 6.9681910774977815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making that replaces a single global cutoff with an evidence-conditioned, instance-adaptive criterion. For each test instance, proximal exemplars are retrieved in an embedding space; their predictive distributions are fused via Dempster-Shafer theory. The resulting fused belief acts as a per-instance thresholding mechanism. Because the supporting evidences are explicit, decisions are transparent and auditable. Experiments on CIFAR-10/100 with BiT and ViT backbones show higher or comparable uncertainty-aware performance with materially fewer confidently incorrect outcomes and a sustainable review load compared with applying threshold on prediction entropy. Notably, only a few evidences are sufficient to realize these gains; increasing the evidence set yields only modest changes. These results indicate that evidence-conditioned tagging provides a more reliable and interpretable alternative to fixed prediction entropy thresholds for operational uncertainty-aware decision-making.

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