Hybrid PSO-SVM for Financial Early-Warning Model of Small and Medium-Sized Enterprises

DOI

10.2991/aebmr.k.210319.020How to use a DOI?

Keywords

financial early warning model, particle swarm optimization (PSO), support vector machine (SVM)

Abstract

As feature subset selection and parameter tuning are important for the performance of SVM-based models, a PSO-SVM model was provided which uses particle swarm optimization (PSO) to optimize both a feature subset and parameters of SVM simultaneously so as to improve the prediction result. Finally, the PSO-SVM model was applied to a financial early warning model, which shows a better performance than the pure SVM- based model.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xinke Chong
PY  - 2021
DA  - 2021/03/22
TI  - Hybrid PSO-SVM for Financial Early-Warning Model of Small and Medium-Sized Enterprises
BT  - Proceedings of the 6th International Conference on Financial Innovation and Economic Development (ICFIED 2021)
PB  - Atlantis Press
SP  - 107
EP  - 114
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.210319.020
DO  - 10.2991/aebmr.k.210319.020
ID  - Chong2021
ER  -