Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey
Applied and Computational Mathematics
Volume 7, Issue 4, August 2018, Pages: 197-202
Received: Sep. 16, 2018;
Published: Sep. 18, 2018
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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
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.
Subjective Well-Being Prediction Using Data Mining Techniques: Evidence from Chinese General Social Survey, Applied and Computational Mathematics.
Vol. 7, No. 4,
2018, pp. 197-202.
Gilboa, I., & Schmeidler, D. (2001). A cognitive model of individual well-being. Social Choice and Welfare, 18 (2), 269-288.
Liu, J., Xiong, M., & Su, Y. (2012). National sense of happiness in the economic growth period: a study based on CGSS data. Social Sciences in China, 12, 82-102.
Zhao, W. (2012). Economic inequality, status perceptions, and subjective well-being in China’s transitional economy. Research in Social Stratification and Mobility, 30 (4), 433-450.
Hu, A. (2013). Public sector employment, relative deprivation and happiness in adult urban Chinese employees. Health Promot Int, 28 (3), 477-486.
Liu, J., Xiong, M., & Su, Y. (2013). National happiness at a time of economic growth: a tracking study based on CGSS data. Social Sciences in China, 34 (4), 20-37.
Cheng, Z. (2014). The effects of employee involvement and participation on subjective wellbeing: evidence from urban China. Social Indicators Research, 118 (2), 457-483.
Applasamy, V., Gamboa, R. A., Al-Atabi, M., & Namasivayam, S. (2014). Measuring happiness in academic environment: a case study of the school of engineering at taylor’s university (Malaysia). Procedia-Social and Behavioral Sciences, 123, 106-112.
Coverdale, G. E., & Long, A. F. (2015). Emotional wellbeing and mental health: an exploration into health promotion in young people and families. Perspectives in Public Health, 135 (1), 27-36.
Chen, T., & Guestrin, C. (2016). Xgboost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, August 13-17, pp. 785-794.
Kasperczuk, A., & Dardzińska, A. (2016). Comparative evaluation of the different data mining techniques used for the medical database. Acta Mechanica Et Automatica, 10 (3), 233-238.
Salehan, M., & Dan, J. K. (2016). Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
Wang, D., Zhang, Z., Bai, R., & Mao, Y. (2017). A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring. Journal of Computational and Applied Mathematics, 329, 307-321.
He, H. L., Zhang, W. Y., & Zhang, S. (2018). A novel ensemble method for credit scoring: adaption of different imbalance ratios. Expert Systems with Applications, 98, 105-117.
Dong, P. Y., & Eun-Kyoung, O. L. (2004). Religiousness/spirituality and subjective well-being among rural elderly Whites, African Americans, and Native Americans. Journal of Human Behavior in the Social Environment, 10 (1), 191-211.
Dong, P. Y. (2006). Factors affecting subjective well-being for rural elderly individuals. Journal of Religion and Spirituality in Social Work Social Thought, 25 (2), 59-75.
Luhmann, M., Schimmack, U., & Eid, M. (2011). Stability and variability in the relationship between subjective well-being and income. Journal of Research in Personality, 45 (2), 186-197.
Liang, Y., & Wang, P. (2014). Influence of prudential value on the subjective well-being of Chinese urban-rural residents. Social Indicators Research, 118 (3), 1249-1267.
Qian, Y., & Knoester, C. (2015). Parental status and subjective well-being among currently married individuals in China. Journal of Family Issues, 36 (10), 1351-1376.
Oshio, T., Nozaki, K., & Kobayashi, M. (2011). Relative income and happiness in Asia: evidence from nationwide surveys in China, Japan, and Korea. Social Indicators Research, 104 (3), 351-367.
Zhang, Z., & Treiman, D. J. (2013). Social origins, hukou conversion, and the wellbeing of urban residents in contemporary China. Social Science Research, 42 (1), 71-89.
Cheng, Z., Wang, H., & Smyth, R. (2014). Happiness and job satisfaction in urban China: a comparative study of two generations of migrants and urban locals. Urban Studies, 51 (10), 2160-2184.
Otoiu, A., Titan, E., & Dumitrescu, R. (2014). Are the variables used in building composite indicators of well-being relevant? Validating composite indexes of well-being. Ecological Indicators, 46, 575-585.
Wang, Y., Wu, Y., & He, W. (2016). Development of classification models for predicting happiness: a data mining approach. International Journal of Digital Content Technology and its Applications, 10 (3), 1-10.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29 (5), 1189-1232.
Hand, D. J., & Kelly, M. G. (2002). Superscorecards. Ima Journal of Management Mathematics, 13 (4), 273-281.
Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural network: a market comparative study. Decision Support Systems, 37 (4), 543-558.
Li, X., Ying, W., Tuo, J., & Li, B. (2004). Applications of classification trees to consumer credit scoring methods in commercial banks. In Proccedings of IEEE International Conference on Systems, Man and Cybernetics, Hague, Netherlands, October 10-13, Vol. 5, pp. 4112-4117.
Fawcett, T. (2004). ROC graphs: notes and practical considerations for researchers. Pattern Recognition Letters, 31 (8), 1-38.