Scholarlink

Domain name management and DNS operation are critical as the basics of Internet operation. Developing a long-term investment plan is essential for stable and sustainable domain name management. Since revenues rely on the number of registered domain names, future registration estimation is a crucial managerial task. This study aims to develop a model that predicts 1st-year registration renewal of individual domain names, focusing on the General-use JP domain names (accounting for approx. 70% of all JP domain registrations). Using decision trees and their advanced machine learning algorithms, the model incrementally adds and verifies features, achieving an accuracy of 80.6%.

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Prediction of 1st Year Registration Renewal of General-Use JP Domain Names with the Use of Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Takaharu Ui,
  • Shota Ikehara,
  • Kentaro Mori,
  • Takeshi Ozaki,
  • Hiroo Hirose

摘要

Domain name management and DNS operation are critical as the basics of Internet operation. Developing a long-term investment plan is essential for stable and sustainable domain name management. Since revenues rely on the number of registered domain names, future registration estimation is a crucial managerial task. This study aims to develop a model that predicts 1st-year registration renewal of individual domain names, focusing on the General-use JP domain names (accounting for approx. 70% of all JP domain registrations). Using decision trees and their advanced machine learning algorithms, the model incrementally adds and verifies features, achieving an accuracy of 80.6%.