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Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8083-2864
Statisticon AB.
Stena Line AB.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth group)
2019 (English)In: Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR , 2019, Vol. 105, p. 53-65Conference paper, Published paper (Refereed)
Abstract [en]

Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method where a conformal predictor is trained on each data source independently, and where the prediction intervals are then combined into a single interval. We call the approach Non-Disclosed Conformal Prediction (NDCP), and we evaluate it on a regression dataset from the UCI machine learning repository using support vector regression as the underlying machine learning algorithm, with varying number of data sources and sizes. The results show that the proposed method produces conservatively valid prediction intervals, and while we cannot retain the same efficiency as when all data is used, efficiency is improved through the proposed approach as compared to predicting using a single arbitrarily chosen source.

Place, publisher, year, edition, pages
PMLR , 2019. Vol. 105, p. 53-65
Series
Proceedings of Machine Learning Research, ISSN 2640-3498
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-400588OAI: oai:DiVA.org:uu-400588DiVA, id: diva2:1381811
Conference
Conformal and Probabilistic Prediction and Applications
Funder
Swedish Foundation for Strategic Research , HASTEAvailable from: 2019-12-27 Created: 2019-12-27 Last updated: 2019-12-27

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Spjuth, OlaGauraha, Niharika

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CiteExportLink to record
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