Title:	Uncertainty-aware Evaluation of Machine Learning Performance in binary Classification Tasks
Authors:	Sperling, Leo Lämmer, Simon Hagen, Hans Scheuermann, Gerik Gillmann, Christina
Citation:	Journal of WSCG. 2022, vol. 30, no. 1-2, p. 63-71.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://hdl.handle.net/11025/49395
ISSN:	1213-6972 (print) 1213-6964 (on-line)
Keywords:	hodnotící opatření;nejistota-uvědomění;strojové učení
Keywords in different language:	evaluation measures;uncertainty-awareness;machine learning
Abstract in different language:	Machine learning has become a standard tool in computer vision. Nowadays, neural networks are one of the most prominent representatives in this class of algorithms that usually require training and evaluation to work as desired. There exist a variety of evaluation metrics to determine the quality of a trained neural network, which are usually threshold dependent. This results in massive changes in the resulting evaluation when the threshold is changed slightly. Further, measurements of uncertainty such as resulting from Bayesian approaches, are not considered in this analysis. In this paper, we present evaluation metrics for machine learning approaches that are able to attach a probability distribution to the utilized threshold and include uncertainty measures. We demonstrate the applicability of our approach by applying the defined metrics to a real-world example where a Bayesian neural network has been used to predict stroke lesions.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 30, Number 1-2 (2021)

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