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Classification With Reject Option Using Conformal Prediction
Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
Dept. of Computer Science and Informatics, Jönköping University.
School of Electrical Engineering and Computer Science, Royal Institute of Technology.
Dept. of Computer Science and Informatics, Jönköping University.
2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Cham, 2018.
HSV kategori
Identifikatorer
URN: urn:nbn:se:hb:diva-15265OAI: oai:DiVA.org:hb-15265DiVA, id: diva2:1259677
Konferanse
Pacific-Asia Conference of Knowledge Discovery and Data Mining, Melbourne, Australia, May 15-18, 2018
Prosjekter
DASTARD
Forskningsfinansiär
Knowledge Foundation, 20150185Tilgjengelig fra: 2018-10-30 Laget: 2018-10-30 Sist oppdatert: 2018-11-16bibliografisk kontrollert

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