A New Nonlinear Multiregression Model Based on the Lower and Upper Integrals

被引：0
|
作者：
Chu J. [1 ,2 ]
Wang Z. [3 ]
Shi Y. [2 ,4 ]
Leung K.-S. [5 ]
机构：
[1] School of Management, University of Chinese Academy of Sciences, Beijing
[2] Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing
[3] Department of Mathematics, University of Nebraska at Omaha, Omaha, 68182, NE
[4] College of Information Science and Technology, University of Nebraska at Omaha, Omaha, 68182, NE
[5] Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
来源：
Annals of Data Science | 2014年 / 1卷 / 1期
基金：
中国国家自然科学基金;
关键词：
Data mining; Linear programming; Multiregression; Nonlinear integrals; Signed efficiency measures; Soft computing;
D O I：
10.1007/s40745-014-0008-6
中图分类号：
学科分类号：
摘要：
A new nonlinear multiregression model based on a pair of extreme nonlinear integrals, lower and upper integrals, is established in this paper. A complete data set of predictive attributes and the relevant objective attribute is required for estimating the regression coefficients. Due to the nonadditivity of the model, a genetic algorithm combined with the pseudo gradient search is adopted to search the optimized solution in the regression problem. Applying such a nonlinear multiregression model, an interval prediction for the value of the objective attribute can be made once a new observation of predictive attributes is available. © 2014, Springer-Verlag GmbH Berlin Heidelberg.
引用
收藏
页码：109 / 125
页数：16
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