Convex support vector regression

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2024
Major/Subject
Mcode
Degree programme
Language
en
Pages
13
858-870
Series
European Journal of Operational Research, Volume 313, issue 3
Abstract
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues by introducing the convex support vector regression (CSVR) method, which effectively combines the key elements of convex regression and support vector regression. Numerical experiments demonstrate the performance of CSVR in prediction accuracy and robustness that compares favorably with other state-of-the-art methods.
Description
Funding Information: The authors would like to thank the three anonymous reviewers for their helpful comments. We acknowledge the computational resources provided by the Aalto Science-IT project. Zhiqiang Liao gratefully acknowledges financial support from the Foundation for Economic Education (Liikesivistysrahasto) [grant no. 210038] and the Jenny and Antti Wihuri Foundation [grant no. 00220201]. Sheng Dai gratefully acknowledges financial support from the Foundation for Economic Education (Liikesivistysrahasto) [grant no. 220074] and the OP Group Research Foundation [grant no. 20230008]. Publisher Copyright: © 2023 The Author(s)
Keywords
Convex regression, Overfitting, Regularization, Robustness and sensitivity analysis, Support vector regression
Other note
Citation
Liao, Z, Dai, S & Kuosmanen, T 2024, ' Convex support vector regression ', European Journal of Operational Research, vol. 313, no. 3, pp. 858-870 . https://doi.org/10.1016/j.ejor.2023.05.009