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Signed-Error Conformal Regression
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2014 (English)In: Advances in Knowledge Discovery and Data Mining 18th Pacific-Asia Conference, PAKDD 2014 Tainan, Taiwan, May 13-16, 2014 Proceedings, Part I, Springer , 2014, 224-236 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper suggests a modification of the Conformal Prediction framework for regression that will strengthen the associated guarantee of validity. We motivate the need for this modification and argue that our conformal regressors are more closely tied to the actual error distribution of the underlying model, thus allowing for more natural interpretations of the prediction intervals. In the experimentation, we provide an empirical comparison of our conformal regressors to traditional conformal regressors and show that the proposed modification results in more robust two-tailed predictions, and more efficient one-tailed predictions.

Place, publisher, year, edition, pages
Springer , 2014. 224-236 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8443 LNCS
Keyword [en]
Conformal Prediction, Regression, Prediction Intervals, Machine Learning
National Category
Computational Mathematics Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-7183DOI: 10.1007/978-3-319-06608-0_19Local ID: 2320/13765ISBN: 978-3-319-06607-3 (print)OAI: oai:DiVA.org:hb-7183DiVA: diva2:887891
Conference
18th Pacific-Asia Conference, PAKDD 2014 Tainan, Taiwan, May 13-16, 2014
Note

Sponsorship:

Swedish Foundation for Strategic Research through the project High-Performance Data Mining for Drug Effect Detection (IIS11-0053) and the Knowledge Foundation through the project Big Data Analytics by Online Ensemble Learning (20120192).

Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

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Linusson, HenrikJohansson, UlfLöfström, Tuve
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