Finding Dominant Factor That Affects Crude Birth Rates in Japanese Prefectures

Yukari Shirota, Kenji Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2020 6th IEEE International Conference on Information Management, ICIM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-77
Number of pages5
ISBN (Electronic)9781728157702
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes
Event6th IEEE International Conference on Information Management, ICIM 2020 - London, United Kingdom
Duration: 27 Mar 202029 Mar 2020

Publication series

Name2020 6th IEEE International Conference on Information Management, ICIM 2020

Conference

Conference6th IEEE International Conference on Information Management, ICIM 2020
Country/TerritoryUnited Kingdom
CityLondon
Period27/03/2029/03/20

Keywords

  • cross validation
  • fertility rate
  • random forest regression
  • xgboosted regression

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