Random projection ensemble conformal prediction for high-dimensional classification

In classification problems, many models with superior performance fail to provide confidence estimates or intervals for each prediction. This lack of reliability poses risks in real-world applications, making these models difficult to trust. Conformal prediction, as distribution-free and model-free approaches with finite-sample coverage guarantee, have recently been widely used to construct prediction sets for classification models. However, traditional conformal prediction methods only produce set-valued results without specifying a definitive predicted class. Particularly in complex settings, these methods fail to assist models in effectively addressing challenges such as high dimensionality, resulting in ambiguous prediction sets with low statistical efficiency, i.e. the prediction sets contain many false classes. In this study, a novel Ensemble Conformal Prediction algorithm based on Random Projection and a designed voting strategy, RPECP, is developed to tackle these challenges. Initially, a procedure for selecting the approximately oracle random projections and classifiers is executed to best leverage the internal information and structure of the data. Subsequently, based on the approximately oracle random projections and underlying classifiers, conformal prediction is performed on new test samples in a lower-dimensional space, resulting in multiple independent prediction sets. Finally, an accurate predicted class and a precise prediction set with high coverage and statistical efficiency are produced through a designed voting strategy. Compared to several base classifiers, RPECP obtain higher classification accuracy; against other conformal prediction algorithms, it achieves less ambiguous prediction sets with fewer false classes while guaranteeing high coverage. For illustration, this paper demonstrates RPECP's superiority over other methods in four cases: two high-dimensional settings and two real-world datasets.