处理使用基于XGBoost的链式方程回归方法的多重归算的缺失数据
Zhao Jinbo1, Li Yufu1, Mo Haitao2
1Shangwan Coal Mine, Ejin Horo Banner, Ordos, China.
Frontiers in artificial intelligence
|April 18, 2025
概括
本研究介绍了XGBoost-MICE方法来处理缺少的矿山通风数据,显示高精度和稳定的收. 该方法通过改进数据归算,有效地优化了智能矿山通风系统.
科学领域:
- 数据科学数据科学数据科学
- 采矿工程 采矿工程 采矿工程
背景情况:
- 缺少数据是监控和优化矿山通风系统的一个重大挑战.
- 准确的通风参数对于矿山安全和运营效率至关重要.
研究的目的:
- 引入和验证 XGBoost-MICE 方法,用于赋值缺失的矿山通风数据.
- 在不同的缺失数据速率和代数下评估方法的准确性和有效性.
主要方法:
- 利用了来自香煤矿的历史通风系统数据.
- 模拟场景的缺失数据率分别为5%,10%和15%.
- 采用了XGBoost-MICE (链式方程的多重推算) 算法,进行了30次和50次的代.
主要成果:
- 平均平方误差 (MSE) 从0.0445增加到0.3254,因为缺失率从5%上升到15%.
- 解释变量从0.988309下降到0.943267,缺失数据增加.
- 平均绝对误差 (MAE) 增加了0.29; 对于特定属性,MSE和MAE在六次代后趋同.
结论:
- 采用XGBoost-MICE方法证明了矿山通风数据的高归算准确性和稳定趋同.
- 这种方法为优化智能矿山通风系统提供了强大的技术支持.
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