A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications

Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng, Xizhao Wang

Abstract:

Quantifying the uncertainty of supervised learning models plays an important role
in making more reliable predictions. Epistemic uncertainty, which usually is due
to insufficient knowledge about the model, can be reduced by collecting more data
or refining the learning models. Over the last few years, scholars have proposed
many epistemic uncertainty handling techniques which can be roughly grouped into
two categories, i.e., Bayesian and ensemble. This paper provides a comprehensive
review of epistemic uncertainty learning techniques in supervised learning over the
last five years. As such, we, first, decompose the epistemic uncertainty into bias and
variance terms. Then, a hierarchical categorization of epistemic uncertainty learning
techniques along with their representative models is introduced. In addition, several
applications such as computer vision (CV) and natural language processing (NLP)
are presented, followed by a discussion on research gaps and possible future research
directions.