DUEn: An Ensemble Framework Enhanced by Distribution-Free Uncertainty for Regression

The main objective of ensemble learning is to aggregate multiple models to better capture complex data distributions. Various ensemble techniques, including bagging and boosting, have been investigated and widely embraced in both research and practical applications. In this work, we enhance ensemble learning by incorporating distribution-free uncertainty inspired by conformal prediction. Conformal prediction allows us to quantify any model’s uncertainty rigorously with valid coverage guarantees under lenient assumptions of the data distribution. We propose a novel ensemble learning framework called Distribution-Free Uncertainty-Aware Ensemble Framework (DUEn) for regression tasks which uses the information from distribution-free uncertainty in the form of intervals to benefit final point predictions and makes outputs more accurate and robust. Moreover, we propose a weighted interval agreement approach that aggregates base learners considering the degrees of uncertainty of their predictions. Experiments conducted on multiple data sets from different domains illustrate that DUEn is capable of enhancing the accuracy of regression by effectively using data while considering each base learner’s distribution-free uncertainty.