Abstract
Boosting algorithms are a class of general methods used to improve the general performance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.
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This work was supported by the National High-tech Research and Development Program of China (No. 2003AA412110).
Ling YU was born in 1974. She received the M.S. degree in control theory and application from Zhejiang University in 1998, and now is an on-job doctoral candidate of Zhejiang University. Her current research interests are in the area of nonlinear identification and multivariate regression analysis.
Tiejun WU was born in 1950. He received the Ph. D. degree in engineering from Zhejiang University in 1988. He is currently with Zhejiang university as professor. His research interest is focused on complex systems intelligent control and optimization.
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Yu, L., Wu, T. Boosting the partial least square algorithm for regression modelling. J. Control Theory Appl. 4, 257–260 (2006). https://doi.org/10.1007/s11768-006-5231-z
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DOI: https://doi.org/10.1007/s11768-006-5231-z