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Classification with Reject Option Using Conformal Prediction
Univ Boras, Dept Informat Technol, Boras, Sweden..
Jonkoping Univ, Dept Comp Sci & Informat, Jonkoping, Sweden..
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
Jonkoping Univ, Dept Comp Sci & Informat, Jonkoping, Sweden..
2018 (English)In: Advances in Knowledge Discovery and Data Mining, PAKDD 2018, PT I / [ed] Phung, D Tseng, VS Webb, GI Ho, B Ganji, M Rashidi, L, Springer, 2018, Vol. 10937, 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. Vol. 10937, p. 94-105
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 10937
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-235161DOI: 10.1007/978-3-319-93034-3_8ISI: 000443224400008Scopus ID: 2-s2.0-85049360232ISBN: 9783319930336 (print)OAI: oai:DiVA.org:kth-235161DiVA, id: diva2:1248880
Conference
22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), JUN 03-06, 2018, Deakin Univ, Melbourne, Australia
Note

QC 20180917

Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2020-01-22Bibliographically approved

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Boström, Henrik

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