Multiregression based on upper and lower nonlinear integrals

Authors:
JinFeng Wang;KwongSak Leung;KinHong Lee;ZhenYuan Wang;Jun Xu
Affiliations:
College of Educational Information Technology, The South China Normal University, Guang Zhou, 510631, People's Republic of China;Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong SAR;Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong SAR;Department of Mathematics, University of Nebraska at Omaha, Omaha, NE 68182;College of Educational Information Technology, The South China Normal University, Guang Zhou, 510631, People's Republic of China
Venue:
International Journal of Intelligent Systems
Year:
2012

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Abstract

A new nonlinear multiregression model based on a pair of extreme nonlinear integrals, upper and lower nonlinear integrals with respect to signed fuzzy measure, is established in this paper. A data set with the predictive features and the relevant objective feature is required for estimating the regression coefficients. Owing to the nonadditivity of the model, a multiobjective optimization using genetic algorithm is adopted to search for the optimized solution in the regression problem. Applying such a nonlinear multiregression model, an interval prediction for the value of the objective feature can be made once a new observation of predictive features is available. We apply our model on synthetic data and weather problem. The results testify the performance of the multiregression based on upper and lower nonlinear integrals. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.