Abstract
For precise estimation with soft sensors, it is necessary to remove outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear function with multidimensional input. In this paper we propose a practical approach to outlier detection using Support Vector Regression, which reduces computational cost and defines outlier threshold appropriately. We apply this approach to both test and industrial datasets for validation.
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
Lin, B., Recke, B., Knudsen, J., Jørgensen, S.: A systematic approach for soft sensor development. Computers and Chemical Engineering 31, 419–425 (2007)
Pell, R.J.: Multiple outlier detection for multivariate calibration using robust statistical techniques. Chemometrics and Intelligent Laboratory Systems 52, 87–104 (2000)
Jordaan, E.M., Smits, G.F.: Robust Outlier Detection using SVM Regression. In: Proceeding. 2004 IEEE International Joint Conference on Neural Network (2004)
Nakayama, H., Yun, Y.: Support Vector Regression Based on Goal Programming and Multi-objective Programming. In: IJCNN 2006, Neural Networks (2006)
Mangasarian, O.L.: Nonlinear Programming. McGraw-Hill, New York (1969)
Brownlee, K.A.: Statistical Theory and Methodology in Science and Engineering, pp. 491–500. Wiley, New York (1960)
Rousseeuw, P.J., Baxter, M.A.: Robust Regression and Outlier Detection. John Wiley & Sons, Inc., New York (1987)
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
Nishiguchi, J., Kaseda, C., Nakayama, H., Arakawa, M., Yun, Y. (2009). Practical Approach to Outlier Detection Using Support Vector Regression. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_121
Download citation
DOI: https://doi.org/10.1007/978-3-642-02490-0_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
eBook Packages: Computer ScienceComputer Science (R0)