Confident prediction is highly relevant in machine learning; for example, in applications such as medical diagnoses, wrong prediction can be fatal. For classification, there already exist procedures that allow to not classify data when the confidence in their prediction is weak. This approach is known as classification with reject option. In this paper, we provide a new methodology for this approach. Predicting a new feature via a confidence set, we ensure an exact control of the probability of classification. Moreover, we show that this methodology can be implemented easily, in a semi-supervised way, and has attractive theoretical and numerical properties.

中文翻译：

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半监督学习中用于分类的插件置信集的一致性

置信预测与机器学习高度相关；例如，在医疗诊断等应用中，错误的预测可能是致命的。对于分类，已经存在允许在预测的置信度较弱时不对数据进行分类的程序。这种方法称为带有拒绝选项的分类。在本文中，我们为这种方法提供了一种新的方法论。通过置信集预测新特征，我们确保准确控制分类概率。此外，我们表明这种方法可以以半监督的方式轻松实现，并且具有有吸引力的理论和数值特性。