TY - CONF A1 - Lodes, Lukas A1 - Schiendorfer, Alexander A2 - Do, Phuc A2 - Michau, Gabriel A2 - Ezhilarasu, Cordelia T1 - Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks BT - Proceedings of the European Conference of the PHM Society 2022 N2 - 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. KW - machine Learning KW - classification KW - uncertainty KW - estimaton KW - neural network Y1 - 2022 UR - https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/3130 UR - https://doi.org/10.36001/phme.2022.v7i1.3331 UR - https://nbn-resolving.org/urn:nbn:de:bvb:573-31307 SN - 978-1-936263-36-3 SP - 294 EP - 305 PB - PHM Society CY - State College ER -