Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method

Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method

This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for addressing missing data in mine ventilation parameters. Using historical ventilation system data from Shangwan Coal Mine, scenarios with different missing rates (5, 10, and 15%) and iteration numbers (30 and...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhao Jinbo, Li Yufu, Mo Haitao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Artificial Intelligence
Subjects:
mine ventilation parameters
XGBoost
multiple imputation
data completeness
intelligent management
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1553220/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.frontiersin.org/articles/10.3389/frai.2025.1553220/full

Similar Items