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

页码：109 / 125

页数：16

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