Proceedings of the 1st International Symposium on Education, Culture and Social Sciences (ECSS 2019)

Research on Social Stability Risk Assessment Method

Authors
Yongliang Xiao, Xiangbao Li, Wenbin Liu, Canwei He
Corresponding Author
Yongliang Xiao
Available Online April 2019.
DOI
10.2991/ecss-19.2019.50How to use a DOI?
Keywords
Social Stability Risk; Machine Learning; Dimensionality reduction; Support Vector Machines.
Abstract

The social stability risk assessment system of major decisions projects will directly affect the evaluation results. Therefore, the design of evaluation model is a problem to be considered comprehensively, and a scientific and reasonable evaluation index system is the premise of objective and accurate evaluation of social stability risk. In this paper, the evaluation model is proposed based on learning strategy. Firstly, we use a dimensionality reduction method based on labeled information and unlabeled information to extract effective evaluation information. Then we use support vector machine to get the evaluation result of social stability risk

Copyright
© 2019, 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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Volume Title
Proceedings of the 1st International Symposium on Education, Culture and Social Sciences (ECSS 2019)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
April 2019
ISBN
10.2991/ecss-19.2019.50
ISSN
2352-5398
DOI
10.2991/ecss-19.2019.50How to use a DOI?
Copyright
© 2019, 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/).

Cite this article

TY  - CONF
AU  - Yongliang Xiao
AU  - Xiangbao Li
AU  - Wenbin Liu
AU  - Canwei He
PY  - 2019/04
DA  - 2019/04
TI  - Research on Social Stability Risk Assessment Method
BT  - Proceedings of the 1st International Symposium on Education, Culture and Social Sciences (ECSS 2019)
PB  - Atlantis Press
SP  - 249
EP  - 252
SN  - 2352-5398
UR  - https://doi.org/10.2991/ecss-19.2019.50
DO  - 10.2991/ecss-19.2019.50
ID  - Xiao2019/04
ER  -