Automatic Error Detection in Gaussian Processes Regression Modeling for Production Scheduling

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearch

Authors

In the application field of production, scheduling with dispatching rules is facing the problem that no rule performs globally better than any other. Therefore, machine learning techniques can be used to calculate estimates of rule performances and select the best rule for each system state. A number of estimates are of poor quality and lead to a wrong selection of rules. Motivated by this problem, to further stabilize the selection approach a general approach, to automatically detect ‘faulty’ estimates from regression models is introduced and analyzed in this paper. Therefore, different models are learned and if their estimates differ strongly, it is likely that at least one model delivers poor estimates.
Additionally, a difference-threshold for our example data is defined. As a machine learning technique, we use Gaussian process regression with different covariance functions (kernels). The results have shown that our automatic detection
Original languageEnglish
Title of host publicationADVCOMP 2011 : The Fifth International Conference on Advanced Engineering Computing and Applications in Sciences
Number of pages6
PublisherIARIA XPS Press
Publication date2011
Pages66-71
ISBN (print)978-1-61208-172-4
Publication statusPublished - 2011
Externally publishedYes
Event5th International Conference on Advanced Engineering Computing and Applications in Sciences - ADVCOMP 2011 - Lissabon, Portugal
Duration: 20.11.201125.11.2011
Conference number: 5
https://www.iaria.org/conferences2011/ADVCOMP11.html