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.
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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
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DOI: https://doi.org/10.1007/0-387-28111-8_6
Publisher Name: Springer, Boston, MA
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