ArticleFrontiers in artificial intelligence2025
Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A stacking ensemble with Pareto optimization for scalable electricity theft detection via hybrid data repair and lightweight deployment.Scientific reports · 2026Article
- Development and internal validation of an interpretable machine learning model to predict coagulopathy following extracorporeal membrane oxygenation: a retrospective multicenter study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Article
- Machine learning-based prediction of post-thrombotic syndrome in unprovoked lower extremity deep vein thrombosis: a two-center study incorporating uric acid and clinical risk factors.European journal of medical research · 2026Article
- The impact of intention to adopt generative AI for exercise information on exercise adherence among Chinese college students: the mediating role of autonomous motivation and network analysis.Frontiers in public health · 2026Article
- The relationship between Tai Chi practice, Psychological Resilience, and depression in older adults: an integrated study of mediation and network analysis.Frontiers in public health · 2026Article
- Bayesian LSTM-Based Missing Data Estimation and Flexural Strength Assessment for Determination of Novel Smart Mobility Pavement Materials: Mg(OH)Materials (Basel, Switzerland) · 2025Article
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3 authors.
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Abstract
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 50) were simulated to validate the accuracy and effectiveness of the approach. The results demonstrate that as the missing rate increased from 5 to 15%, the Mean Squared Error (MSE) rose from 0.0445 to 0.3254, while the Explained Variance decreased from 0.988309 to 0.943267. Additionally, the Mean Absolute Error (MAE) increased by 0.29. Iteration experiments on the "frictional resistance per 100 meters" attribute showed convergence of MSE and MAE after six iterations. Overall, the XGBoost-MICE method exhibited high imputation accuracy and stable convergence across various missing data scenarios, providing robust technical support for optimizing intelligent mine ventilation systems.
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