An uncertain support vector machine with imprecise observations

Zhongfeng Qin () and Qiqi Li
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Zhongfeng Qin: Beihang University
Qiqi Li: Beihang University

Fuzzy Optimization and Decision Making, 2023, vol. 22, issue 4, No 3, 629 pages

Abstract: Abstract Support vector machines have been widely applied in binary classification, which are constructed based on crisp data. However, the data obtained in practice are sometimes imprecise, in which classical support vector machines fail in these situations. In order to handle such cases, this paper employs uncertain variables to describe imprecise observations and further proposes a hard margin uncertain support vector machine for the problem with imprecise observations. Specifically, we first define the distance from an uncertain vector to a hyperplane and give the concept of a linearly α-separable data set. Then, based on maximum margin criterion, we propose an uncertain support vector machine for the linearly α-separable data set, and derive the corresponding crisp equivalent forms. New observations can be classified through the optimal hyperplane derived from the model. Finally, a numerical example is given to illustrate the uncertain support vector machine.

Keywords: Uncertain variable; Imprecise observations; Uncertain support vector machine; Classification problem; Maximum margin criterion (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10700-022-09404-0

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