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On the calibration of aggregated conformal predictors
Department of Information Technology, University of Borås, Sweden.
Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
Department of Computer and Systems Sciences, Stockholm University, Sweden.
Högskolan i Jönköping, Tekniska Högskolan, JTH, Datateknik och informatik, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.ORCID-id: 0000-0003-0412-6199
Vise andre og tillknytning
2017 (engelsk)Inngår i: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, Machine Learning Research , 2017, s. 154-173Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical validity is conditional on additional assumptions that require further clarification. In this paper, we show why validity is not automatic for aggregated conformal predictors, and provide a revised definition of aggregated conformal predictors that gains approximate validity conditional on properties of the underlying learning algorithm.

sted, utgiver, år, opplag, sider
Machine Learning Research , 2017. s. 154-173
Emneord [en]
Confidence Predictions, Conformal Prediction, Classification, Ensembles
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-38123OAI: oai:DiVA.org:hj-38123DiVA, id: diva2:1163973
Konferanse
The 6th Symposium on Conformal and Probabilistic Prediction with Applications, (COPA 2017), 13-16 June, 2017, Stockholm, Sweden
Tilgjengelig fra: 2017-12-08 Laget: 2017-12-08 Sist oppdatert: 2019-08-22bibliografisk kontrollert

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