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 a reject option 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 a reject option. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection. Moreover, we define the existing architectures for models with a reject option, describe the standard learning strategies to train such models and relate traditional machine learning techniques to rejection. Additionally, we review strategies to evaluate a model's predictive and rejective quality. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.
翻译:机器学习模型总能做出预测, 即使它很可能是不准确的。 在许多决策支持应用程序中, 这种行为应该避免, 错误可能会产生严重后果 。 尽管在1970年已经研究过, 机器学习与拒绝选项最近引起了兴趣 。 这个机器学习子字段让机器学习模型在可能出错时可以避免做出预测 。 这个调查旨在提供机器学习与拒绝选项的概览 。 我们引入了导致两种拒绝、 模糊和新颖拒绝的条件 。 此外, 我们用拒绝选项来定义模型的现有结构, 描述培训这些模型的标准学习策略, 并将传统机器学习技术与拒绝联系起来 。 此外, 我们审查评估模型预测和拒绝质量的战略 。 最后, 我们提供相关应用域的实例, 并展示拒绝机器学习与其他机器学习研究领域的关系 。