Exploring Subjective Well-Being Factors with Support Vector Machine
Keywords:Use about five key words or phrases in alphabetical order, Separated by Semicolon.
National-level Subjective well-being (SWB) is known to be associated with six traditional factors, that is, GDP per capita, social support, healthy life expectancy, social freedom, generosity, and absence of corruption, but debates persist about the variability of these six factors. Whether the predicting in SWB is based only on these six factors or not? Are there any country-specific factors? Thus, we examined these two questions with the data sets from World Happiness Report and OECD database for U.S.. By control-ling the other factors except only one, we employed Support Vector Machine (SVM) to identify the weight of each factor without worrying about the limitations caused by the small size of the sample in years. We found that another three factors (protein con-sumption, fruit consumption and physician ratio among all employee) in addition to the six traditional factors can also affect na-tional-level SWB in U.S.; Moreover, the power of SVW in prediction is as high as 95.08%, which is much greater than that present-ed by the linear regression models (74.3%).
 Sachs, Jeffrey D., and John W, McArthur, â€œThe millennium project: a plan for meeting the millennium development goalsâ€, The Lancet, Vol-365-9456, (2005), pp.347-353.
 Helliwell, John F., Richard Layard, and Jeffrey Sachs. World happiness report . (2012).
 Diener, E., Diener, M. and Diener, C., â€œFactors predicting the subjective well-being of nationsâ€, Journal of Personality and Social Psychology, Vol 69-5, (1995), pp.851-864.
 Diener, E., â€œSubjective well-being: The science of happiness and a proposal for a national indexâ€, American Psychologist, Vol-55-1, (2000), pp.34-43.
 Diener, E., Oishi, S. and Lucas, R., â€œPersonality, Culture, and Subjective Well-Being: Emotional and Cognitive Evaluations of Lifeâ€, Annual Review of Psychology, Vol-54-1, (2003), pp.403-425.
 Diener, E., Oishi, S. and Lucas, R., â€œNational accounts of subjective well-beingâ€, American Psychologist, Vol-70-3, (2012), pp.234-242.
 OECD-Total. (2013). Quarterly National Accounts, 2013(3), pp.310-311.
 Welsch, H., â€œPreferences over Prosperity and Pollution: Environmental Valuation based on Happiness Surveysâ€, Kyklos, Vol-55-4, (2012), pp.473-494.
 Welsch, H., â€œEnvironment and happiness: Valuation of air pollution using life satisfaction dataâ€, Ecological economics,Vol-58-4, (2006), pp.801-813.
 Costanza, R., Fisher, B., Ali, S., Beer, C., Bond, L., Boumans, R., Danigelis, N., Dickinson, J., Elliott, C., Farley, J., Gayer, D., Glenn, L., Hudspeth, T., Mahoney, D., McCahill, L., McIntosh, B., Reed, B., Rizvi, S., Rizzo, D., Simpatico, T. and Snapp, R. â€œQuality of life: An approach integrating opportunities, human needs, and subjective well-beingâ€, Ecological Economics, Vol-61-2.3, (2007), pp.267-276.
 Clark, A. E., & Oswald, A. J., â€œUnhappiness and unemploymentâ€ The Economic Journal, Vol-104-424, (1994), pp.648-659.
 Oswald, A. J. â€œHappiness and economic performanceâ€, The economic journal, Vol-107-445, (1997), pp.1815-1831.
 Lang, I., Wallace, R. B., Huppert, F. A., & Melzer, D., â€œModerate alcohol consumption in older adults is associated with better cognition and well-being than abstinenceâ€, Age and ageing, Vol-36-3, (2007), pp.256-261.
 McCann, S. â€œSubjective well-being, personality, demographic variables, and American state differences in smoking prevalenceâ€, Nicotine & Tobacco Research, Vol-12-9, (2010), pp.895-904.
 Sachs, Jeffrey D., Richard Layard, and John F. Helliwell. World Happiness Report 2018. No. id: 12761. 2018.
 Cummins, R. A., â€œPersonal income and subjective well-being: A reviewâ€, Journal of Happiness Studies,Vol-1-2, (2000), pp.133-158.
 Vapnik, V., â€œThe nature of statistical learning theoryâ€, Springer science & business media, (2013), Vol-114-434, (1994), pp.645-669.
 Chen-Chia Chuang, Shun-Feng Su, Jin-Tsong Jeng and Chih-Ching Hsiao., â€œRobust support vector regression networks for function approximation with outliersâ€ IEEE Transactions on Neural Networks, Vol-13-6, (2002), pp.1322-1330.
 Hong, W. C., Dong, Y., Chen, L. Y., & Wei, S. Y., â€œSVR with hybrid chaotic genetic algorithms for tourism demand forecastingâ€ Applied Soft Computing, Vol-11-2, (2011), pp.1881-1890.
 Syam, N., & Sharma, A. â€œWaiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practiceâ€, Industrial Marketing Management, Vol-6-9, (2018), pp.135-146.