Being able to assess the confidence of individual predictions in machine learning models is crucial for decision making scenarios. Specially, in critical applications such as medical diagnosis, security, and unmanned vehicles, to name a few. In the last years, complex predictive models have had great success in solving hard tasks and new methods are being proposed every day. While the majority of new developments in machine learning models focus on improving the overall performance, less effort is put on assessing the trustworthiness of individual predictions, and even to a lesser extent, in the context of sensor fusion. To this end, we build and test multi-view and single-view conformal models for heterogeneous sensor fusion. Our models provide theoretical marginal confidence guarantees since they are based on the conformal prediction framework. We also propose a multi-view semi-conformal model based on sets intersection. Through comprehensive experimentation, we show that multi-view models perform better than single-view models not only in terms of accuracy-based performance metrics (as it has already been shown in several previous works) but also in conformal measures that provide uncertainty estimation. Our results also showed that multi-view models generate prediction sets with less uncertainty compared to single-view models.

翻译：能够评估机器学习模型中单个预测的置信度，对于决策场景至关重要，尤其是在医疗诊断、安防和无人驾驶等关键应用中。近年来，复杂预测模型在解决困难任务方面取得了巨大成功，新方法层出不穷。尽管机器学习模型的大多数新进展都聚焦于提升整体性能，但对单个预测可信度的评估投入较少，而在传感器融合领域则更甚。为此，我们构建并测试了用于异构传感器融合的多视角与单视角共形模型。这些模型基于共形预测框架，提供理论上的边际置信度保证。我们还提出了一种基于交集的多视角半共形模型。通过全面实验，我们证明多视角模型不仅在基于准确率的性能指标上（如先前多项研究所示）优于单视角模型，而且在提供不确定性估计的共形度量上也表现更佳。结果还表明，与单视角模型相比，多视角模型生成的预测集不确定性更小。