统计与决策2023，Vol.39Issue(23)：22-27,6.DOI:10.13546/j.cnki.tjyjc.2023.23.004
基于LHD与GPR的机器学习超参数建模及优化
Machine Learning Hyperparameter Modeling and Optimization Based on LHD and GPR
摘要
Abstract
For the nonlinear,high-order interaction and complex constraints between machine learning hyperparameters and algorithm performance,this paper proposes a modeling and optimization approach based on Gaussian process regression and ge-netic algorithm.The Latin hypercube sampling is used to take points in the hyperparameter feasible region,the Gaussian process regression model between the hyperparameter and the algorithm performance is established,and the genetic algorithm is utilized to optimize the established model.The simulation study shows that this method can better fit the complex relationship between hyper-parameters and algorithm performance in the hyperparameter feasible region.Compared with the traditional response surface method,the proposed method can achieve global optimization with fewer experiments in the hyperparameter feasible region,there-by effectively improving the efficiency and accuracy of optimization.关键词
超参数优化/机器学习/超拉丁方设计/高斯过程回归/遗传算法Key words
hyperparameter optimization/machine learning/Latin hypercube design/Gaussian process regression/genetic algorithm分类
信息技术与安全科学引用本文复制引用
王方成,刘玉敏,崔庆安..基于LHD与GPR的机器学习超参数建模及优化[J].统计与决策,2023,39(23):22-27,6.基金项目
国家自然科学基金资助项目(U1904211 （）
71672182 （）
71571168) （）
国家社会科学基金资助项目(20BTJ059) （20BTJ059）
