Publication
Title
Machine learning with a reject option : a survey
Author
Abstract
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model's predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.
Language
English
Source (journal)
Machine learning. - Boston, Mass., 1986, currens
Publication
Dordrecht : Springer , 2024
ISSN
0885-6125 [print]
1573-0565 [online]
DOI
10.1007/S10994-024-06534-X
Volume/pages
113 (2024) , p. 3073-3110
ISI
001194621500001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 02.05.2024
Last edited 09.09.2024
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