Synergy Conformal Prediction
Conformal Prediction is a machine learning methodology that produces valid prediction regions under mild conditions. Ensembles of conformal predictors have been proposed to improve the informational efficiency of inductive conformal predictors by combining p-values, however, the validity of such methods has been an open problem. We introduce Synergy Conformal Prediction which is an ensemble method that combines monotonic conformity scores, and is capable of producing valid prediction intervals. We study the applicability in two scenarios; where data is partitioned in order to reduce the total model training time, and where an ensemble of different machine learning methods is used to improve the overall efficiency of predictions. We evaluate the method on 10 data sets and show that the synergy conformal predictor produces valid predictions and improves informational efficiency as compared to inductive conformal prediction and existing ensemble methods. The results indicate that synergy conformal prediction has advantageous properties compared to contemporary approaches, and we also envision that it will have an impact in Big Data and federated environments.