Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey

Leibao Zhang; Yanli Fan; Wenyu Zhang; Shuai Zhang

doi:doi:10.11648/j.acm.20180704.13

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Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey

Leibao Zhang, Yanli Fan, Wenyu Zhang, Shuai Zhang

Published in Applied and Computational Mathematics (Volume 7, Issue 4)

Received: 16 September 2018 Published: 18 September 2018

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Abstract

Data mining techniques have attracted increasing attentions recently and played more and more important roles in various domains. However, few studies have used these prevalent techniques to explore the rules of subjective well-being for individuals. In this study, a prevalent data mining method, XGBoost, is applied to predict the subjective well-being according to various predictive factors. Feature selection step is implemented to further improve the prediction results and reduce the computational complex based on the importance calculated by XGBoost. An authoritative academic database, Chinese General Social Survey, is used for providing an evidence for classification prediction performance. Moreover, five benchmark models, i.e., logistic regression, support vector machine, decision tree, random forest, and gradient boosting decision tree, are used for comparative analysis based on three evaluation metrics, Accuracy, AUC and F-score. The experimental results indicate that XGBoost outperforms other benchmark models, and feature selection step can improve the prediction performance and reduce the computational time to some extent. In reality, using data mining methods can deeply explore the rule of subjective well-being based on various predictive features, and provide an overwhelming support for improving subjective well-being. Therefore, the methods used in this study are effective and the results provide a support for making society more harmonious.

Published in	Applied and Computational Mathematics (Volume 7, Issue 4)
DOI	10.11648/j.acm.20180704.13
Page(s)	197-202
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Subjective Well-Being, Data Mining, XGBoost, Classification Prediction

References

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APA Style

Leibao Zhang, Yanli Fan, Wenyu Zhang, Shuai Zhang. (2018). Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey. Applied and Computational Mathematics, 7(4), 197-202. https://doi.org/10.11648/j.acm.20180704.13

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ACS Style

Leibao Zhang; Yanli Fan; Wenyu Zhang; Shuai Zhang. Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey. Appl. Comput. Math. 2018, 7(4), 197-202. doi: 10.11648/j.acm.20180704.13

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AMA Style

Leibao Zhang, Yanli Fan, Wenyu Zhang, Shuai Zhang. Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey. Appl Comput Math. 2018;7(4):197-202. doi: 10.11648/j.acm.20180704.13

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@article{10.11648/j.acm.20180704.13,
  author = {Leibao Zhang and Yanli Fan and Wenyu Zhang and Shuai Zhang},
  title = {Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey},
  journal = {Applied and Computational Mathematics},
  volume = {7},
  number = {4},
  pages = {197-202},
  doi = {10.11648/j.acm.20180704.13},
  url = {https://doi.org/10.11648/j.acm.20180704.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180704.13},
  abstract = {Data mining techniques have attracted increasing attentions recently and played more and more important roles in various domains. However, few studies have used these prevalent techniques to explore the rules of subjective well-being for individuals. In this study, a prevalent data mining method, XGBoost, is applied to predict the subjective well-being according to various predictive factors. Feature selection step is implemented to further improve the prediction results and reduce the computational complex based on the importance calculated by XGBoost. An authoritative academic database, Chinese General Social Survey, is used for providing an evidence for classification prediction performance. Moreover, five benchmark models, i.e., logistic regression, support vector machine, decision tree, random forest, and gradient boosting decision tree, are used for comparative analysis based on three evaluation metrics, Accuracy, AUC and F-score. The experimental results indicate that XGBoost outperforms other benchmark models, and feature selection step can improve the prediction performance and reduce the computational time to some extent. In reality, using data mining methods can deeply explore the rule of subjective well-being based on various predictive features, and provide an overwhelming support for improving subjective well-being. Therefore, the methods used in this study are effective and the results provide a support for making society more harmonious.},
 year = {2018}
}

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TY  - JOUR
T1  - Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey
AU  - Leibao Zhang
AU  - Yanli Fan
AU  - Wenyu Zhang
AU  - Shuai Zhang
Y1  - 2018/09/18
PY  - 2018
N1  - https://doi.org/10.11648/j.acm.20180704.13
DO  - 10.11648/j.acm.20180704.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 197
EP  - 202
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20180704.13
AB  - Data mining techniques have attracted increasing attentions recently and played more and more important roles in various domains. However, few studies have used these prevalent techniques to explore the rules of subjective well-being for individuals. In this study, a prevalent data mining method, XGBoost, is applied to predict the subjective well-being according to various predictive factors. Feature selection step is implemented to further improve the prediction results and reduce the computational complex based on the importance calculated by XGBoost. An authoritative academic database, Chinese General Social Survey, is used for providing an evidence for classification prediction performance. Moreover, five benchmark models, i.e., logistic regression, support vector machine, decision tree, random forest, and gradient boosting decision tree, are used for comparative analysis based on three evaluation metrics, Accuracy, AUC and F-score. The experimental results indicate that XGBoost outperforms other benchmark models, and feature selection step can improve the prediction performance and reduce the computational time to some extent. In reality, using data mining methods can deeply explore the rule of subjective well-being based on various predictive features, and provide an overwhelming support for improving subjective well-being. Therefore, the methods used in this study are effective and the results provide a support for making society more harmonious.
VL  - 7
IS  - 4
ER  -

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Author Information

Leibao Zhang

School of Public Finance and Taxation, Zhejiang University of Finance and Economics, Hangzhou, China
Yanli Fan

School of Public Finance and Taxation, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Shuai Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Leibao Zhang, Yanli Fan, Wenyu Zhang, Shuai Zhang. (2018). Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey. Applied and Computational Mathematics, 7(4), 197-202. https://doi.org/10.11648/j.acm.20180704.13

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ACS Style

Leibao Zhang; Yanli Fan; Wenyu Zhang; Shuai Zhang. Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey. Appl. Comput. Math. 2018, 7(4), 197-202. doi: 10.11648/j.acm.20180704.13

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AMA Style

Leibao Zhang, Yanli Fan, Wenyu Zhang, Shuai Zhang. Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey. Appl Comput Math. 2018;7(4):197-202. doi: 10.11648/j.acm.20180704.13

Copy | Download

@article{10.11648/j.acm.20180704.13,
  author = {Leibao Zhang and Yanli Fan and Wenyu Zhang and Shuai Zhang},
  title = {Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey},
  journal = {Applied and Computational Mathematics},
  volume = {7},
  number = {4},
  pages = {197-202},
  doi = {10.11648/j.acm.20180704.13},
  url = {https://doi.org/10.11648/j.acm.20180704.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180704.13},
  abstract = {Data mining techniques have attracted increasing attentions recently and played more and more important roles in various domains. However, few studies have used these prevalent techniques to explore the rules of subjective well-being for individuals. In this study, a prevalent data mining method, XGBoost, is applied to predict the subjective well-being according to various predictive factors. Feature selection step is implemented to further improve the prediction results and reduce the computational complex based on the importance calculated by XGBoost. An authoritative academic database, Chinese General Social Survey, is used for providing an evidence for classification prediction performance. Moreover, five benchmark models, i.e., logistic regression, support vector machine, decision tree, random forest, and gradient boosting decision tree, are used for comparative analysis based on three evaluation metrics, Accuracy, AUC and F-score. The experimental results indicate that XGBoost outperforms other benchmark models, and feature selection step can improve the prediction performance and reduce the computational time to some extent. In reality, using data mining methods can deeply explore the rule of subjective well-being based on various predictive features, and provide an overwhelming support for improving subjective well-being. Therefore, the methods used in this study are effective and the results provide a support for making society more harmonious.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey
AU  - Leibao Zhang
AU  - Yanli Fan
AU  - Wenyu Zhang
AU  - Shuai Zhang
Y1  - 2018/09/18
PY  - 2018
N1  - https://doi.org/10.11648/j.acm.20180704.13
DO  - 10.11648/j.acm.20180704.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 197
EP  - 202
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20180704.13
AB  - Data mining techniques have attracted increasing attentions recently and played more and more important roles in various domains. However, few studies have used these prevalent techniques to explore the rules of subjective well-being for individuals. In this study, a prevalent data mining method, XGBoost, is applied to predict the subjective well-being according to various predictive factors. Feature selection step is implemented to further improve the prediction results and reduce the computational complex based on the importance calculated by XGBoost. An authoritative academic database, Chinese General Social Survey, is used for providing an evidence for classification prediction performance. Moreover, five benchmark models, i.e., logistic regression, support vector machine, decision tree, random forest, and gradient boosting decision tree, are used for comparative analysis based on three evaluation metrics, Accuracy, AUC and F-score. The experimental results indicate that XGBoost outperforms other benchmark models, and feature selection step can improve the prediction performance and reduce the computational time to some extent. In reality, using data mining methods can deeply explore the rule of subjective well-being based on various predictive features, and provide an overwhelming support for improving subjective well-being. Therefore, the methods used in this study are effective and the results provide a support for making society more harmonious.
VL  - 7
IS  - 4
ER  -

Copy | Download