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

Songlin Du, Ling Luo, Ilia Nouretdinov, Uwe Aickelin
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 266:336-358, 2025.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v266-du25a, title = {DUEn: An Ensemble Framework Enhanced by Distribution-Free Uncertainty for Regression}, author = {Du, Songlin and Luo, Ling and Nouretdinov, Ilia and Aickelin, Uwe}, booktitle = {Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {336--358}, year = {2025}, editor = {Nguyen, Khuong An and Luo, Zhiyuan and Papadopoulos, Harris and Löfström, Tuwe and Carlsson, Lars and Boström, Henrik}, volume = {266}, series = {Proceedings of Machine Learning Research}, month = {10--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v266/main/assets/du25a/du25a.pdf}, url = {https://proceedings.mlr.press/v266/du25a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T DUEn: An Ensemble Framework Enhanced by Distribution-Free Uncertainty for Regression %A Songlin Du %A Ling Luo %A Ilia Nouretdinov %A Uwe Aickelin %B Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2025 %E Khuong An Nguyen %E Zhiyuan Luo %E Harris Papadopoulos %E Tuwe Löfström %E Lars Carlsson %E Henrik Boström %F pmlr-v266-du25a %I PMLR %P 336--358 %U https://proceedings.mlr.press/v266/du25a.html %V 266 %X 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.
APA
Du, S., Luo, L., Nouretdinov, I. & Aickelin, U.. (2025). DUEn: An Ensemble Framework Enhanced by Distribution-Free Uncertainty for Regression. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:336-358 Available from https://proceedings.mlr.press/v266/du25a.html.

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