과제정보
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2021S1A5C2A03088191)
참고문헌
- Bae, G.R., Moon, H.J., Lee, J.I., Park, M.N., and Park, A.R., Comparative Analysis of Low Fertility Policy and the Public Perceptions using Text-Mining Methodology, Journal of Digital Convergence, 2021, Vol. 19, No. 12, pp. 29-42. https://doi.org/10.14400/JDC.2021.19.12.029
- Boser, B.E., Guyon, I.M., and Vapnik, V.N., A training algorithm for optimal margin classifiers, In Proceedings of the fifth annual workshop on Computational learning theory, 1992, pp. 144-152.
- Breiman, L., Random forests, Machine Learning, 2001, Vol. 45, pp. 5-32. https://doi.org/10.1023/A:1010933404324
- Chen, T. and Guestrin, C., Xgboost: A scalable tree boosting system, In Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
- Cho, Y.T., Won, S.H., Kim, S.Y., Ahn, J.H., and Choi, J.H., Study on Causes of Low Birthrate in Gyeonggi Province and Forecasting Birth Trends, Graduate School of Public Health, Seoul National University, 2016.
- Choi, S.M., Analysis of Factors Influencing the fertility rate of metropolitan governments: Focusing on economic and policy factors, Journal of Governance Studies, 2021, Vol. 16, No. 4, pp. 65-100. https://doi.org/10.16973/JGS.2021.16.4.003
- Defense Advanced Research Projects Agency, Explainable Artificial Intelligence (XAI)[Internet], https://www.darpa.mil/program/explainable-artificial-intelligence.
- Ha, J.K., An Economic Analysis of Low Fertility in Korea: Focusing on Income Inequality and Cost of Education, The Review of Social&Economic Studies, 2012, vol., no.39, pp.137-174
- Joo, S.M., Ock, S.H., and Hwang, K.T., Forecasting Birthrate Change based on Big Data, Informatization Policy, 2019, Vol. 26, No. 4, pp. 20-35.
- Kim, B.R. and Kwon, O.Y., Explainable Artificial Intelligence (XAI) technology that goes beyond the limitations of medical artificial intelligence, Korea Health Industry Development Institute, 2021, Vol. 340, pp. 5-10.
- Kim, K.A., An Analysis of Factors Affecting the Birth Rate of Married Women, Culture and Convergence, 2017, Vol. 39, No. 6, pp. 895-924. https://doi.org/10.33645/cnc.2017.12.39.6.895
- Korea Institute of Public Finance, Policy Design Using Machine Learning: Focusing on the Determinants of Childbirth, Financial Forum, 2019, Vol. 279, pp. 12-35.
- Lee, C.H., Did Pro-natal Policy in Korea Fail?: A Decomposition of Fertility Change from 2000 to 2016, The Korean Journal of Economic Studies, 2018, Vol. 66, No. 3, pp. 5-42. https://doi.org/10.22841/KJES.2018.66.3.001
- Ministry of National Defense, Defense White Paper 2016, 2016.
- NABO, 2019~2050 NABO long-term financial outlook, 2018.
- Oh, S.W. and Kwon, Y.J., Factors Influencing the Fertility Rate by Local Government in Korea: Focused on Socio-cultural Factors, Economic Factors, and Policy Factors, Public Policy Review, 2018, Vol. 32, No. 1, pp. 55-81. https://doi.org/10.17327/IPPA.2018.32.1.003
- Park, E.J.L. and Cho, S.Z., KoNLPy: Korean natural language processing in Python, Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology, 2014, Vol. 6. Chuncheon Korea.
- Park, S.M., Na, C.W., Choi, M.S., Lee, D.H., and On, B.W., KNU Korean Sentiment Lexicon: Bi-LSTM-based Method for Building a Korean Sentiment Lexicon, Journal of Intelligence and Information Systems, 2018, Vol. 4, pp. 219-240.
- Statistics Korea, Population trend survey, 2022.
- Won, S.Y. and Choi, Y.H., The Determinants of the Fertility Rate of Local Government in Korea: Focusing on Factors related to the Cost for Children, The Korea Association for Policy Studies, 2018, Vol. 27, No. 3, pp. 231-268.