Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
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DOI：
10.1007/s10994-021-05946-3
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发表时间：
2021-03-08
期刊：
影响因子：
7.5
通讯作者：
Waegeman, Willem
中科院分区：
文献类型：
--
作者：
Huellermeier, Eyke;Waegeman, Willem
不确定性的概念在机器学习中非常重要，并构成了机器学习方法的关键要素。根据统计学的传统，不确定性一直被认为是标准概率和概率预测的同义词。然而，由于机器学习与实际应用的相关性不断增加，以及安全要求等相关问题，机器学习学者最近发现了新的问题和挑战，这些问题可能需要新的方法发展。特别是，这包括区分（至少）两种不同类型的不确定性的重要性，通常被称为任意性和认识性。在本文中，我们介绍了机器学习中的不确定性主题，并概述了迄今为止处理不确定性的尝试，特别是将这种区别形式化。
The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.