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Classification with reject option using conformal prediction
Department of Information Technology, University of Borås, Borås, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.ORCID iD: 0000-0003-0412-6199
School of Electrical Engineering and Computer Science, Royal Institute of Technology, Kista, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.ORCID iD: 0000-0003-0274-9026
2018 (English)In: Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I, Springer, 2018, p. 94-105Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a practically useful means of interpreting the predictions produced by a conformal classifier. The proposed interpretation leads to a classifier with a reject option, that allows the user to limit the number of erroneous predictions made on the test set, without any need to reveal the true labels of the test objects. The method described in this paper works by estimating the cumulative error count on a set of predictions provided by a conformal classifier, ordered by their confidence. Given a test set and a user-specified parameter k, the proposed classification procedure outputs the largest possible amount of predictions containing on average at most k errors, while refusing to make predictions for test objects where it is too uncertain. We conduct an empirical evaluation using benchmark datasets, and show that we are able to provide accurate estimates for the error rate on the test set. 

Place, publisher, year, edition, pages
Springer, 2018. p. 94-105
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10937
Keywords [en]
Data mining, Errors, Forecasting, Testing, Uncertainty analysis, Benchmark datasets, Classification procedure, Conformal predictions, Cumulative errors, Empirical evaluations, Error rate, Test object, Test sets, Classification (of information)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-41260DOI: 10.1007/978-3-319-93034-3_8ISI: 000443224400008Scopus ID: 2-s2.0-85049360232ISBN: 9783319930336 (print)OAI: oai:DiVA.org:hj-41260DiVA, id: diva2:1242267
Conference
22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne; Australia; 3 June 2018 through 6 June 2018
Funder
Knowledge FoundationAvailable from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-09-20Bibliographically approved

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CiteExportLink to record
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  • apa
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Output format
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