Related papers: Machine Learning with a Reject Option: A survey

Machine Learning with a Reject Option: A survey

URL: http://arxiv.org/abs/2107.11277v3
Date: Wed, 21 Feb 2024 10:10:40 GMT
Title: Machine Learning with a Reject Option: A survey
Authors: Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis
Abstract summary: 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. We review and categorize strategies to evaluate a model's predictive and rejective quality.
Score: 18.43771007525432
License: http://creativecommons.org/licenses/by/4.0/
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.

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