Title :
Boosting the PLS Algorithm for Regressive Modelling
Author :
Yu, Ling ; Wu, Tiejun
Author_Institution :
Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou
Abstract :
Boosting algorithms are a class of general methods used to improve the generalization 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 regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better generalization performance over the PLS algorithm
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); least squares approximations; regression analysis; boosting; gasoline octane number prediction; generalization; partial least squares; regression analysis; Automation; Boosting; Boosting; generalization; partial least square (PLS); regression;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713302
