Multi-View Conformal Learning for Heterogeneous Sensor Fusion
Enrique Garcia-Ceja
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.