Multi-View Conformal Learning for Heterogeneous Sensor Fusion
This work addresses the need for trustworthy individual predictions in critical applications like medical diagnosis and unmanned vehicles, though it is incremental as it extends existing conformal prediction methods to sensor fusion.
The paper tackled the problem of assessing prediction confidence in machine learning, particularly for heterogeneous sensor fusion, by building and testing multi-view and single-view conformal models, showing that multi-view models perform better in accuracy and generate prediction sets with less uncertainty.
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.