Uncertainty reduction of class conditional conformal prediction via multi-inputs aggregation
Jean-Baptiste Fermanian, Mohamed Hebiri, Joseph Salmon
Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an innovative refinement to these methods, specifically tailored for scenarios where multiple predictive observations of a single instance are available. Our approach is particularly motivated by applications in citizen science, where multiple images of the same plant or animal are captured by individuals. Our method integrates the information from each observation into conformal prediction, enabling a reduction in the size of the predicted label set while preserving the required conditional coverage guarantee. The approach is based on the aggregation of conformal p-values computed from each view. By exploiting the exact distribution of these p-values, we refine standard aggregation strategies such as majority voting or template-based methods, and we provide a general framework for adapting our approach to other contexts. We evaluate our method on simulated and real data, with a particular focus on Pl@ntNet, a prominent citizen science platform that facilitates the collection and identification of plant species through user-submitted images.