Effects of Collinearity on Combining Neural Networks
作者:
SHERIF HASHEM,
期刊:
Connection Science
(Taylor Available online 1996)
卷期:
Volume 8,
issue 3-4
页码: 315-336
ISSN:0954-0091
年代: 1996
DOI:10.1080/095400996116794
出版商: Taylor & Francis Group
关键词: Collinearity;Optimal Linear Combination;Model Selection;Function Approximation;Mixture Of Experts
数据来源: Taylor
摘要:
Collinearity or linear dependency among a number of estimators may pose a serious problem when combining these estimators. The corresponding outputs of a number of neural networks NNs , which are trained to approximate the same quantity or quantities , may be highly correlated. Thus, the estimation of the optimal weights for combining such networks may be subjected to the harmful effects of collinearity, which results in a final model with inferior generalizations ability compared with the individual networks. In this paper, we investigate the harmful effects of collinearity on the estimation of the optimal weights for combining a number on NNs. We discuss an approach for selecting the component networks in order to improve the generalization ability of the combined model. Our experimental results demonstrate significant improvements in the generalization ability of a combined model as a result of the proper selection of the component networks. The approximation accuracy of the combined model is compared with two common alternatives: the apparent best network or the simple average of the corresponding outputs of the networks.
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