Abstract
This chapter gives an overview, based on the experience from the Dow Chemical Company, of the importance of variable selection to build robust models from industrial datasets. A quick review of variable selection schemes based on linear techniques is given. A relatively simple fitness inheritance scheme is proposed to do nonlinear sensitivity analysis that is especially effective when combined with Pareto GP. The method is applied to two industrial datasets with good results.
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
Banzhaf, W., Nordin, P., Keller, R., and Francone, F. (1998). Genetic Programming: An Introduction, San Francisco, CA: Morgan Kaufmann.
Cherkassky V, Mulier, F., 1998, “Learning from data, Concepts, Theory and Methods”, Wiley Interscience, ISBN 0-471-15493-8.
Draper, N. R. and Smith, H. (1981) Applied Regression Analysis, Second Edition, New York, NY: Wiley.
Eriksson, L., Johansson, E., Wold, N., and Wold, S. (2001). Multi and Megavariate Data Analysis: Principles and Applications, Umea, Sweden, Umetrics Academy.
Francone, F. et al (2004). Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming, Genetic and Evolutionary Computation Conference, Late Breaking Papers.
Gilbert, R.J., Goodacre, R., Shann, B., Taylor, J., Rowland, J.J. and Kell, D.B., Genetic Programming-Based Variable Selection for High-Dimensional Data, in J.R. Koza et al., editors, Genetic Programming 1998: Proceedings of the Third Annual Conference (GP-98), Madison, WI 22–25 July 1998, Morgan Kaufmann, San Fransisco, CA.
Ohnson, H.E, Gilbert, R.J., Winson, M.K., Goodacre, R., Smith, A.R., Rowand, J.J., Hall, M.A. and Kell, D.B. Explanatory Analysis of he Metabolome Using Genetic Programming of Simple, Interpretable Rules, in Genetic Programming and Evolvable Machines, Vol 1 (2000)
Jordaan, E., Kordon, A., Smits, G., and Chiang L. (2004). Robust Inferential Sensors based on Ensemble of predictors generated by Genetic Programming, In Proceedings of PPSN 2004, pp. 522–531, Birmingham, UK.
Kordon, A., Smits, G., Kalos, A., and Jordaan, E. (2003). Robust Soft Sensor Development Using Genetic Programming, In Nature-Inspired Methods in Chemometrics, (R. Leardi-Editor), Amsterdam: Elsevier
Kotanchek, M, Smits, G. and Kordon, A. (2003). Industrial Strength Genetic Programming, In Genetic Programming Theory and Practice, pp 239–258, R. Riolo and B. Worzel (Eds), Boston, MA: Kluwer.
Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press.
RML Technologies, Inc. (2002) Discipulus Owner’s Manual.
Saltelli A., Chan K., and Scott E. (2001). Sensitivity Analysis, Baffins Lane, Chichester, UK: Wiley.
Smits, G. and Kotanchek. (2004), Pareto-Front Exploitation in Symbolic Regression, Genetic Programming Theory and Practice, pp 283–300, R. Riolo and B. Worzel (Eds), Boston, MA: Kluwer.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Smits, G., Kordon, A., Vladislavleva, K., Jordaan, E., Kotanchek, M. (2006). Variable Selection in Industrial Datasets Using Pareto Genetic Programming. In: Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice III. Genetic Programming, vol 9. Springer, Boston, MA. https://doi.org/10.1007/0-387-28111-8_6
Download citation
DOI: https://doi.org/10.1007/0-387-28111-8_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-28110-0
Online ISBN: 978-0-387-28111-7
eBook Packages: Computer ScienceComputer Science (R0)