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Efficient conformal predictor ensembles
Department of Information Technology, University of Borås, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0412-6199
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
2019 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286Article in journal (Refereed) Epub ahead of print
Abstract [en]

In this paper, we study a generalization of a recently developed strategy for generating conformal predictor ensembles: out-of-bag calibration. The ensemble strategy is evaluated, both theoretically and empirically, against a commonly used alternative ensemble strategy, bootstrap conformal prediction, as well as common non-ensemble strategies. A thorough analysis is provided of out-of-bag calibration, with respect to theoretical validity, empirical validity (error rate), efficiency (prediction region size) and p-value stability (the degree of variance observed over multiple predictions for the same object). Empirical results show that out-of-bag calibration displays favorable characteristics with regard to these criteria, and we propose that out-of-bag calibration be adopted as a standard method for constructing conformal predictor ensembles.

Place, publisher, year, edition, pages
Elsevier, 2019.
Keywords [en]
Classification, Conformal prediction, Ensembles, Classification (of information), Forecasting, Statistical methods, Conformal predictions, Conformal predictors, Ensemble strategies, Error rate, P-values, Region size, Calibration, article, bootstrapping, prediction, theoretical study, validity
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:hj:diva-47219DOI: 10.1016/j.neucom.2019.07.113Scopus ID: 2-s2.0-85076549331OAI: oai:DiVA.org:hj-47219DiVA, id: diva2:1382312
Funder
Knowledge Foundation, 20150185Available from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-01-02

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Johansson, Ulf

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