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26

GECCO
2005
Springer

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Symbolic regression in multicollinearity problems

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Symbolic regression in multicollinearity problems

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In this paper the potential of GP-generated symbolic regression for alleviating multicollinearity problems in multiple regression is presented with a case study in an industrial setting. The main advantage of this approach is the potential to produce a simple and stable polynomial model in terms of the original variables. Categories and Subject Descriptors G.3. [Mathematics of Computing]: Probability and statistics– Correlation and regression analysis. General Terms: Experimentation. Keywords Multicollinearity, multiple regression, undesigned data

Flor A. Castillo, Carlos M. Villa

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GECCO 2005 | GP-generated Symbolic Regression | Multicollinearity Problems | Multiple Regression |

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Added	27 Jun 2010
Updated	27 Jun 2010
Type	Conference
Year	2005
Where	GECCO
Authors	Flor A. Castillo, Carlos M. Villa

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