Aleatoric and Epistemic Uncertainty with Random Forests

Shaker, Mohammad Hossein und Hüllermeier, Eyke (April 2020): Aleatoric and Epistemic Uncertainty with Random Forests. International Symposium on Intelligent Data Analysis, Bodenseeforum, Lake Constance, Germany, April 27-29 2020. In: Advances in Intelligent Data Analysis XVIII, Vol. 12080 Cham: Springer. pp. 444-456

Abstract

Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties, referring, respectively, to inherent randomness and a lack of knowledge, with random forests. More specifically, we show how two general approaches for measuring the learner’s aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.

• Item Type: Conference or Workshop Item (Paper)
• Form of publication: Publisher's Version
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• ISSN: 0302-9743
• Place of Publication: Cham
• Language: English