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.

为了评估机器学习模型单个预测的可信度，我们建立和测试了多视角和单视角的异构传感器融合的模型，这些模型基于相合预测框架，提供了理论上的边际置信保证。通过全面实验，我们证明了多视角模型不仅在准确性性能度量方面（已在多个之前的工作中显示），而且在提供不确定性估计的相合度量方面，比单视角模型表现更好。