TY - GEN
T1 - Finding Dominant Factor That Affects Crude Birth Rates in Japanese Prefectures
AU - Shirota, Yukari
AU - Yamaguchi, Kenji
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - We conduct a regression to find a dominant factor that affects crude birth rates in Japan by prefectures. As the traditional regression method, a linear multiple regression is widely used. However, higher accuracy methods with machine learning algorithms have been developed. To find the dominant factor, we use eXtreme Gradient Boosting (XGBoost) and Random Forest which are the decision tree based machine learning algorithms. The results show better accuracies, compared with the traditional linear multiple one. Then, the XGBoost shows that the most dominant factor is the number of marriages, and the second one is the migration rate to the prefecture.
AB - We conduct a regression to find a dominant factor that affects crude birth rates in Japan by prefectures. As the traditional regression method, a linear multiple regression is widely used. However, higher accuracy methods with machine learning algorithms have been developed. To find the dominant factor, we use eXtreme Gradient Boosting (XGBoost) and Random Forest which are the decision tree based machine learning algorithms. The results show better accuracies, compared with the traditional linear multiple one. Then, the XGBoost shows that the most dominant factor is the number of marriages, and the second one is the migration rate to the prefecture.
KW - cross validation
KW - fertility rate
KW - random forest regression
KW - xgboosted regression
UR - http://www.scopus.com/inward/record.url?scp=85085019181&partnerID=8YFLogxK
U2 - 10.1109/ICIM49319.2020.244673
DO - 10.1109/ICIM49319.2020.244673
M3 - Conference contribution
AN - SCOPUS:85085019181
T3 - 2020 6th IEEE International Conference on Information Management, ICIM 2020
SP - 73
EP - 77
BT - 2020 6th IEEE International Conference on Information Management, ICIM 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Information Management, ICIM 2020
Y2 - 27 March 2020 through 29 March 2020
ER -