控制理论与应用2026，Vol.43Issue(8)：1839-1846,8.DOI:10.7641/CTA.2025.40619
改进的加权极限学习机不平衡分类算法及其应用
Improved imbalance classification algorithm using weighted extreme learning machine and its applications
摘要
Abstract
In blast furnace condition classification tasks,traditional classification models often suffer from low accuracy and poor robustness due to limited sample sizes and imbalanced class distributions.To address these challenges,this paper proposes an improved weighted extreme learning machine(WELM)algorithm for imbalanced classification.The method employs a Tent chaotic map-based non-dominated sorting genetic algorithm(CNSGA-Ⅱ)to optimize model parameters.Three key innovations are introduced:Firstly,the Tent chaotic mapping replaces random initialization to enhance the spatial uniformity of initial populations.Secondly,a dynamic boundary constraint mechanism is incorporated during genetic op-erations to suppress boundary violations while accelerating convergence.Thirdly,a dual-objective optimization framework simultaneously maximizes precision and recall rates,thereby improving minority-class recognition without compromising majority-class performance.Experimental validation using real-world blast furnace operation data demonstrates the effec-tiveness of the proposed method in handling imbalanced classification tasks.The results show significant improvements in both classification accuracy and model stability compared to conventional approaches.关键词
不平衡分类/非支配排序遗传算法Ⅱ(NSGA-Ⅱ)/多目标优化/加权极限学习机(WELM)/高炉炉况Key words
imbalanced classification/non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)/multi-objective opti-mization/weighted extreme learning machine(WELM)/blast furnace condition引用本文复制引用
程娇,张亚娴,郭凯,张森,肖文栋..改进的加权极限学习机不平衡分类算法及其应用[J].控制理论与应用,2026,43(8):1839-1846,8.基金项目
国家自然科学基金项目(62173032,61903028,62003038),广东省自然科学基金项目(2022A1515140109),北京市自然科学基金项目(J210005)资助.Supported by the National Natural Science Foundation of China(62173032,61903028,62003038),the National Natural Science Foundation of Guangdong Province(2022A1515140109)and the National Natural Science Foundation of Beijing(J210005). （62173032,61903028,62003038）
