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Predicting with Confidence from Survival Data
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0412-6199
Scania CV AB, Sweden.
2019 (English)In: Conformal and Probabilistic Prediction and Applications / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni Smirnov, 2019, p. 123-141Conference paper, Published paper (Refereed)
Abstract [en]

Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.

Place, publisher, year, edition, pages
2019. p. 123-141
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 105
Keywords [en]
Conformal prediction, survival modeling, random forests.
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:hj:diva-46802OAI: oai:DiVA.org:hj-46802DiVA, id: diva2:1369228
Conference
Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, 9-11 September 2019, Golden Sands, Bulgaria
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-11Bibliographically approved

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Citation style
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