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Synergy Conformal Prediction for Regression
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Farmbio)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Farmbio)ORCID iD: 0000-0002-8083-2864
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Large and distributed data sets pose many challenges for machine learning, including requirements on computational resources and training time. One approach is to train multiple models in parallel on subsets of data and aggregate the resulting predictions. Large data sets can then be partitioned into smaller chunks, and for distributed data the need for pooling can be avoided. Combining results from conformal predictors using synergy rules has been shown to have advantageous properties for classification problems. In this paper we extend the methodology to regression problems, and we show that it produces valid and efficient predictors compared to inductive conformal predictors and cross-conformal predictors for 10 different data sets from the UCI machine learning repository using three different machine learning methods. The approach offers a straightforward and compelling alternative to pooling data, such as when working in distributed environments.

Keywords [en]
Conformal Prediction, Machine Learning, Regression, Synergy, Ensemble Methods
National Category
Engineering and Technology
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:uu:diva-377134OAI: oai:DiVA.org:uu-377134DiVA, id: diva2:1288708
Available from: 2019-02-14 Created: 2019-02-14 Last updated: 2019-02-14Bibliographically approved

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fulltext(312 kB)69 downloads
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Gauraha, NiharikaSpjuth, Ola
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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