- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
MATHEMATICAL CONSIDERATIONS ON THE HYPERPARAMETERS OF XGBOOST AND THEIR APPLICATION TO INFRASTRUCTURE MAINTENANCE AND MANAGEMENT
-
- OYAMA Motoki
- 北海道大学大学院理学院 北海道大学 大学院教育推進機構 高等教育研修センター DX教育連携部門
-
- WAKUDA Yuki
- 北海道大学 大学院教育推進機構 高等教育研修センター DX教育連携部門
-
- YOSHIDA Keisuke
- 北海道大学 大学院教育推進機構 高等教育研修センター DX教育連携部門
-
- ISHIKAWA Yukihiro
- 東京地下鉄株式会社鉄道本部工務部
-
- ENOKIDANI Yuki
- 東京地下鉄株式会社鉄道本部工務部
-
- TANAKA Daisuke
- 東京地下鉄株式会社鉄道本部工務部
-
- KONISHI Shinji
- 東京地下鉄株式会社鉄道本部工務部
Bibliographic Information
- Other Title
-
- XGBoostのハイパーパラメータに関する数学的考察及び, インフラ維持管理への応用
Description
<p>The purpose of this study is to construct a model that can predict the occurrence of deformations in subway tunnels and determine whether or not percussion inspections should be conducted based on the predictions. The XGBoost model, which has been widely used in recent years, is used for this purpose. However, XGBoost has many hyper-parameters, which require time and effort for parameter tuning. Therefore, we categorized the hyperparameters of XGBoost based on mathematical considerations, conducted a grid search using three selected hyperparameters, and evaluated them based on the average value of AUROC. Finally, some properties of the interrelationships among the hyperparameters λ and γ are proved. As a result, we were able to provide some theoretical basis for the output results. Therefore, we report the results here.</p>
Journal
-
- Artificial Intelligence and Data Science
-
Artificial Intelligence and Data Science 3 (J2), 826-847, 2022
Japan Society of Civil Engineers
- Tweet
Details 詳細情報について
-
- CRID
- 1390294113692231168
-
- ISSN
- 24359262
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Disallowed