지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 173 - 188 (16page)
- DOI
- 10.7232/JKIIE.2024.50.3.173
이용수
초록· 키워드
오류제보하기
Korea has a high proportion of self-employed individuals, and their frequent businesses closures are causing serious social problems. Although there are many previous studies on commercial district analysis and long-term survival prediction for self-employed businesses, this area lacks systematic analysis. This study utilized public data and machine learning to perform commercial district analysis and long-term survival prediction for closed restaurants across Seoul. XGBoost (eXtreme Gradient Boosting) was finally selected among seven proposed models. We also utilized SHAP (SHapley Additive exPlanations), one of the explainable artificial intelligence (XAI) techniques, to investigate the influence of variables affecting the proposed model and explore the main factors affecting business closures. As a result, we found that some demographic and geographic factors are important factors for small business survival. Finally, we applied the proposed model to predict new restaurant locations across Seoul, demonstrating its potential usefulness in the decision-making process for self-employed entrepreneurs.
목차
1. 서론
2. 선행연구
3. 데이터
4. 분석 방법론
5. 실험 결과
6. 결론
참고문헌
참고문헌 (71)
참고문헌 신청
Applebaum, W. (1966), Methods for determining store trade areas, market penetration, and potential sales, Journal of marketing Research, 3(2), 127-141.
Bang, J. A., Son, K. M., Lee, S. J., Lee, H. G., and Jo, S. B. (2018), A Study on Predictive Modeling of Public Data: Survival of Fried Chicken Restaurants in Seoul, The Journal of Bigdata, 3(2), 35-49.
Bekkar, M., Djemaa, H. K., and Alitouche, T. A. (2013), Evaluation measures for models assessment over imbalanced data sets, Journal of Information Engineering and Applications, 3(10).
Bradley, A. P. (1997), The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30(7), 1145-1159.
Breiman, L. (2001), Random Forests, Machine Learning, 45, 5-32.
Bang, J. A., Son, K. M., Lee, S. J., Lee, H. G., and Jo, S. B. (2018), A Study on Predictive Modeling of Public Data: Survival of Fried Chicken Restaurants in Seoul, The Journal of Bigdata, 3(2), 35-49.
Bekkar, M., Djemaa, H. K., and Alitouche, T. A. (2013), Evaluation measures for models assessment over imbalanced data sets, Journal of Information Engineering and Applications, 3(10).
Bradley, A. P. (1997), The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30(7), 1145-1159.
Breiman, L. (2001), Random Forests, Machine Learning, 45, 5-32.
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