舰船电子工程2025，Vol.45Issue(1)：132-134,140,4.DOI:10.3969/j.issn.1672-9730.2025.01.026
集成指数梯度与网格搜索的算法公平性优化方法
Optimizing Algorithmic Fairness Through an Integrated Framework of Exponential Gradient and Grid Search Methods
摘要
Abstract
This paper innovatively proposes an integrated framework for optimizing fairness in algorithms using exponential gradient reduction and grid search,achieving optimal dynamic parameter adjustment.After initial exponential gradient reduction,the range and granularity of grid search are adjusted based on the preliminary performance of the model.This method enhances opti-mization efficiency without sacrificing model performance by reducing the size of the parameter space that needs to be explored.The proposed integrated framework allows for precise control of the optimization path and adjustment of weights for different fairness con-straints.Compared to traditional algorithms,our method shows superior performance in fairness metrics such as generalized entropy index,equal opportunity difference,and average odds difference.Experiments conduct on the adult income public dataset demon-strate significant improvements in convergence speed and decision-making fairness,showcasing the adaptability and efficiency of the model fairness framework in complex and varying environments.关键词
算法公平性/指数梯度缩减/网格搜索/机器学习/公平性测度指标Key words
algorithmic fairness/exponential gradient reduction/grid search/machine learning/fairness metrics分类
信息技术与安全科学引用本文复制引用
焦婉妮..集成指数梯度与网格搜索的算法公平性优化方法[J].舰船电子工程,2025,45(1):132-134,140,4.
