Machine learning with a reject option: a survey

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis
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引用次数: 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.

Abstract Image

带有拒绝选项的机器学习:一项调查
机器学习模型总是会做出预测,即使预测可能并不准确。在许多决策支持应用中都应避免这种行为,因为错误会带来严重后果。尽管早在 1970 年就有人研究过,但带有拒绝功能的机器学习最近又引起了人们的兴趣。这一机器学习子领域能让机器学习模型在可能犯错时放弃预测。本研究旨在概述带有拒绝功能的机器学习。我们介绍了导致两种类型拒绝的条件,即模糊性拒绝和新奇性拒绝,并对其进行了细致的形式化。此外,我们还对评估模型预测和拒绝质量的策略进行了回顾和分类。此外,我们还定义了具有拒绝功能的现有模型架构,并介绍了学习此类模型的标准技术。最后,我们提供了相关应用领域的示例,并说明了带拒绝功能的机器学习与其他机器学习研究领域的关系。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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