Abstract
Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniques that the data set used for parameter estimation comes from a stationary joint distribution may not hold in this context because manufacturing processes are subject to physical changes like wear and aging, denoted as process drift. This can cause the estimated model to deviate significantly from the current state of the modeled system. In this paper, we discuss the problem of estimating regression models from drifting processes and we present ensemble regression, an approach that maintains a set of regression models—estimated from different ranges of the data set—according to their predictive performance. We extensively evaluate our approach on synthetic and real-world data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Spitzlsperger, G., Schmidt, C., Ernst, G., Strasser, H., Speil, M.: Fault detection for a via etch process using adaptive multivariate methods. IEEE Transactions on Semiconductor Manufacturing 18(4), 528–533 (2005)
Bunday, B.D., Bishop, M., Donald, W., McCormack, J., Villarrubia, J.S., Vladar, A.E., Dixson, R., Vorburger, T.V., Orji, N.G., Allgair, J.A.: Determination of optimal parameters for cd-sem measurement of line-edge roughness. Metrology, Inspection, and Process Control for Microlithography XVIII 5375(1), 515–533 (2004)
Yue, H.H., Qin, S.J., Wiseman, J., Toprac, A.: Plasma etching endpoint detection using multiple wavelengths for small open-area wafers. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 19(1), 66–75 (2001)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer, Heidelberg (2003)
DiRaddo, R., Girard, P., Chang, S.: Process drift and model-based control of forming operations. In: American Control Conference, 2002. Proceedings of the 2002, vol. 5, pp. 3588–3593 (2002)
Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Machine Learning 1, 317 (1986)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 97–106 (2001)
Street, W.N., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 377–382. ACM, New York (2001)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 226–235. ACM, New York (2003)
Kolter, J., Maloof, M.: Dynamic weighted majority: a new ensemble method for tracking concept drift. In: Third IEEE International Conference on Data Mining (ICDM), November 2003, pp. 123–130 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rosenthal, F., Volk, P.B., Hahmann, M., Habich, D., Lehner, W. (2009). Drift-Aware Ensemble Regression. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-03070-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
eBook Packages: Computer ScienceComputer Science (R0)