Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)

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Research on the Openness of Regions Along the Belt and Road Based on Machine Learning

Taking Liaoning province as an example

Authors
Nan Wang1, *, Feng Li2
1School of Economics Management and Law, Shenyang Institute of Engineering, Shenyang, China
2Guanghua School of Management, Peking University, Beijing, China
*Corresponding author. Email: wangnan@sie.edu.cn
Corresponding Author
Nan Wang
Available Online 30 December 2023.
DOI
10.2991/978-94-6463-326-9_17How to use a DOI?
Keywords
openness; machine learning; K-means; SVM; the Belt and Road
Abstract

The “Belt and Road” project has not only brought Liaoning province a major opportunity and challenge to revitalize its old industrial base but also a brand-new pilot free trade zone. In order to measure the degree of openness of cities in Liaoning province since the establishment of the pilot free trade zone seven years ago, this paper proposes a machine learning-based research method on the degree of openness. The index system of the degree of openness of regions along the “Belt and Road” is constructed from the three dimensions of trade, finance, and investment. The data of 12 cities in Liaoning province from 2016 to 2021 are clustered based on the K-means method, and the clustering results are used as a learning guide to train SVM for classification. The data of 2 other cities in the province were classified using the obtained model, and the openness classification of the 2 cities was obtained. The new combined model can significantly improve the quality of clustering and can be used to study the degree of openness of countries and regions along the “Belt and Road”.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
30 December 2023
ISBN
978-94-6463-326-9
ISSN
2589-4900
DOI
10.2991/978-94-6463-326-9_17How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nan Wang
AU  - Feng Li
PY  - 2023
DA  - 2023/12/30
TI  - Research on the Openness of Regions Along the Belt and Road Based on Machine Learning
BT  - Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)
PB  - Atlantis Press
SP  - 166
EP  - 172
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-326-9_17
DO  - 10.2991/978-94-6463-326-9_17
ID  - Wang2023
ER  -