Affiliations: [a]
School of Economics and Management Science, Beihang University, Beijing, China
| [b] Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing, China
| [c]
School of Reliability and Systems Engineering, Beihang University, Beijing, China
Correspondence:
[*]
Corresponding author. Zhongfeng Qin, School of Economics and Management Science, Beihang University, Beijing 100191, China. E-mail: [email protected].
Abstract: Traditional support vector regression dedicates to obtaining a regression function through a tube, which contains as many as precise observations. However, the data sometimes cannot be imprecisely observed, which implies that traditional support vector regression is not applicable. Motivated by this, in this paper, we employ uncertain variables to describe imprecise observations and build an optimization model, i.e., the uncertain support vector regression model. We further derive the crisp equivalent form of the model when inverse uncertainty distributions are known. Finally, we illustrate the application of the model by numerical examples.
Keywords: Imprecise observations, uncertain variables, support vector regression, uncertainty theory
