钢铁钒钛2025，Vol.46Issue(5)：46-53,64,9.DOI:10.7513/j.issn.1004-7638.2025.05.005
基于重要性度量的脱硫剂加入量预测特征选择方法
A feature selection method for desulfurizer addition prediction based on importance measure
摘要
Abstract
Aiming at the problems of high parameter dimension,strong feature redundancy and weak correlation between target variables and features in hot metal KR desulfurization production process,an integrated feature selection method IMFS(Feature selection based on importance measure)based on im-portance measure is proposed.In the filtering pre-screening stage,the maximal mutual information coef-ficient(MIC)is used to measure the correlation between each parameter and the target variable,as well as the redundancy among each parameter,and the scale of candidate parameters is reduced according to the maximum relevance and minimal redundancy criteria.In the embedded selection stage,the Light-GBM algorithm is introduced as the supporting model for quantifying information contribution and data sensitivity,and the entropy weight method is used to weight and fuse the dual measurement results.Fi-nally,according to the feature importance coefficient,the feature subset is optimized by combining the GBT sequential forward search strategy.The experimental results show that compared with other meth-ods,IMFS has significant advantages in eliminating redundant features and improving prediction accur-acy,and can effectively balance the number of features and prediction accuracy.关键词
脱硫剂加入量/特征选择/重要性系数/双重度量/搜索策略Key words
desulfurizer addition/feature selection/importance coefficient/double metric/search strategy分类
信息技术与安全科学引用本文复制引用
赵海杰,但斌斌,刘洋,任泽宇,都李平,周纯..基于重要性度量的脱硫剂加入量预测特征选择方法[J].钢铁钒钛,2025,46(5):46-53,64,9.基金项目
国家自然科学基金项目(51475340) （51475340）
湖北省重点研发计划项目(2022BAA059) （2022BAA059）
湖北省中央引导地方科技发展专项(2020ZYYD022). （2020ZYYD022）
