现代信息科技2025，Vol.9Issue(7)：29-39,46,12.DOI:10.19850/j.cnki.2096-4706.2025.07.007
基于集成学习的二次协同数据预测及优化方法
Quadratic Collaborative Data Prediction and Optimization Method Based on Ensemble Learning
梁丽娜 1张宇 2张嘉玮3
作者信息
- 1. 北京跟踪与通信技术研究所,北京 100094
- 2. 山东司法警官职业学院,山东 济南 250014
- 3. 河北师范大学计算机与网络空间安全学院,河北 石家庄 050024
- 折叠
摘要
Abstract
The commonly used air quality prediction model has poor prediction effect on unknown conditions,and the actual meteorological conditions have a significant impact on the concentration of air pollutants.In order to reduce the error caused by meteorological conditions to the model prediction of pollution concentration,it is of great significance to obtain a model with good prediction accuracy.Therefore,this paper proposes a quadratic collaborative data prediction and optimization method based on Ensemble Learning.Firstly,it combines the measured data with primary predicted data,and uses the Fancyimpute library for data interpolation for missing and deviating from the normal distribution data.Secondly,the BaggingRegressor model in Ensemble Learning is used to construct a quadratic model,and the influence of meteorological conditions on pollutant concentration is analyzed from the whole to the individual.The voting mechanism is used to synthesize all the prediction results,and the ensemble prediction results are obtained.Finally,a collaborative data prediction model is constructed,and the location relationship and wind direction factors are included for comprehensive prediction.The experimental results show that the method can effectively improve the prediction accuracy of the data,and the collaborative prediction model improves the prediction accuracy of the monitoring points.关键词
Fancyimpute库/数据插补/集成学习/BaggingRegressor模型/二次模型/协同预测模型Key words
Fancyimpute library/data interpolation/Ensemble Learning/BaggingRegressor model/quadratic model/collaborative prediction model分类
信息技术与安全科学引用本文复制引用
梁丽娜,张宇,张嘉玮..基于集成学习的二次协同数据预测及优化方法[J].现代信息科技,2025,9(7):29-39,46,12.
