Unbiased Linear Neural-Based Fusion with Normalized Weighted Average Algorithm for Regression

Authors:
Yunfeng Wu;S. C. Ng
Affiliations:
School of Information Engineering, Beijing University of Posts and Telecommunications, Xi Tu Cheng Road 10, Haidian District, 100876 Beijing, China;School of Science and Technology, The Open University of Hong Kong, 30 Good Shepherd Street, Homantin, Kowloon, Hong Kong
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

Regression is a very important data mining problem. In this paper, we present a new unbiased linear fusion method that combines component predictors so as to solve regression problems. The fusion weighted coefficients assigned are normalized, and updated by estimating the prediction errors between the component predictors and the desired regression values. The empirical results of our regression experiments on five synthetic and four benchmark data sets show that the proposed fusion method improves prediction accuracy in terms of mean-squared error, and also provides the regression curves with better fidelity with respect to normalized correlation coefficients, compared with the popular simple average and weighted average fusion rules.