A paper titled "Customizable Asymmetric Loss Functions for Machine Learning-based Predictive Maintenance" by Lukas Ehrig, Daniel Atzberger, Benjamin Hagedorn, Jan Klimke, and Jürgen Döllner was awarded for the Best Paper award at the 8th International Conference on Condition Monitoring and Diagnosis (CMD 2020) .
The paper introduces an approach of modelling different costs for overestimation and underestimation within machine learning approaches for predicitve maintenance. It describes a configureable cost function that is can be used for training of machine learning techniques in order to optimize predictions that are made using these models.
This project has been partially funded by:
- Federal Ministry of Economic Affairs and Energy under ZIM Program (Project IBDAV)
- German Federal Ministry of Education and Research BMBF: AI Lab for IT-Systems Engineering
