Automatic Error Detection in Gaussian Processes Regression Modeling for Production Scheduling
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
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ADVCOMP 2011: The Fifth International Conference on Advanced Engineering Computing and Applications in Sciences. IARIA XPS Press, 2011. S. 66-71.
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
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TY - CHAP
T1 - Automatic Error Detection in Gaussian Processes Regression Modeling for Production Scheduling
AU - Scholz-Reiter, Bernd
AU - Heger, Jens
N1 - Conference code: 5
PY - 2011
Y1 - 2011
N2 - 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
AB - 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
KW - Engineering
UR - http://www.logdynamics.de/papers_conferences.html?pubID=489
M3 - Article in conference proceedings
SN - 978-1-61208-172-4
SP - 66
EP - 71
BT - ADVCOMP 2011
PB - IARIA XPS Press
T2 - 5th International Conference on Advanced Engineering Computing and Applications in Sciences - ADVCOMP 2011
Y2 - 20 November 2011 through 25 November 2011
ER -