Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks
- Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting.
Author: | Lukas Lodes, Alexander SchiendorferORCiD |
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Language: | English |
Document Type: | Conference Paper |
Conference: | PHME 2022: 7th European Conference of the Prognostics and Health Management Society 2022, Turin (Italy), 06.-08.07.2022 |
Year of first Publication: | 2022 |
published in (English): | Proceedings of the European Conference of the PHM Society 2022 |
Editor(s): | Phuc Do, Gabriel Michau, Cordelia Ezhilarasu |
Publisher: | PHM Society |
Place of publication: | State College |
ISBN: | 978-1-936263-36-3 |
First Page: | 294 |
Last Page: | 305 |
Review: | peer-review |
Open Access: | ja |
Tag: | classification; estimaton; machine Learning; neural network; uncertainty |
URN: | urn:nbn:de:bvb:573-31307 |
Related Identifier: | https://doi.org/10.36001/phme.2022.v7i1.3331 |
Faculties / Institutes / Organizations: | Fakultät Wirtschaftsingenieurwesen |
AImotion Bavaria | |
Licence (German): | Creative Commons BY 3.0 US |
Release Date: | 2023/02/24 |