用主观逻辑量化神经网络校准误差，提升可信度评估
Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory
- 引入主观逻辑融合预测概率，构建信任-怀疑-不确定综合评估框架
- 在MNIST和CIFAR-10上验证，校准后模型可信度显著提升
- 适合医疗、自动驾驶等对可靠性要求高的场景使用
神经网络在关键应用中的可信度至关重要，可靠性、置信度与不确定性在决策中起核心作用。传统指标如准确率和精确率无法捕捉这些特性，尤其在模型过度自信时。本文提出一种新框架，通过将主观逻辑融入期望校准误差（ECE）的评估，量化神经网络的可信度、不信任与不确定性。该方法通过对预测概率聚类并使用合适的融合算子整合意见，实现更全面的评估。在MNIST和CIFAR-10数据集上的实验表明，校准后模型的可信度明显改善。该框架提供更具可解释性和细致性的AI模型评估方式，适用于医疗、自动驾驶等敏感领域。
原文摘要 · Abstract (English)
Trustworthiness in neural networks is crucial for their deployment in critical applications, where reliability, confidence, and uncertainty play pivotal roles in decision-making. Traditional performance metrics such as accuracy and precision fail to capture these aspects, particularly in cases where models exhibit overconfidence. To address these limitations, this paper introduces a novel framework for quantifying the trustworthiness of neural networks by incorporating subjective logic into the evaluation of Expected Calibration Error (ECE). This method provides a comprehensive measure of trust, disbelief, and uncertainty by clustering predicted probabilities and fusing opinions using appropriate fusion operators. We demonstrate the effectiveness of this approach through experiments on MNIST and CIFAR-10 datasets, where post-calibration results indicate improved trustworthiness. The proposed framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems.
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