广东工业大学学报2018，Vol.35Issue(1)：23-28,6.DOI:10.12052/gdutxb.170124
基于贝叶斯最优化的Xgboost算法的改进及应用
The Improvement and Application of Xgboost Method Based on the Bayesian Optimization
摘要
Abstract
When the Xgboost framework is in use, it is often involved in the adjustment of various parameters, and the selection of parameters has a great influence on the classification performance of the model. The traditional parameter optimization method usually first derives a penalty function, and then the empirical or exhaustive method is used to adjust the parameter value to maximize or minimize the penalty function, but often encounters a model without an explicit expression. The optimization of the parameters of this model is very troublesome, also bringing some uncertainty and randomness to the algorithm. The Bayesian optimization algorithm based on Gaussian method (GP) is used to optimize the parameters of the Xgboost framework. A new algorithm, GP_Xgboost, is proposed and experimented by multiple sets of numerical values. The results show that the proposed algorithm is superior to the manual tuning and exhaustive method, which proves the feasibility and effectiveness of the proposed algorithm.关键词
Xgboost算法/模型参数/贝叶斯最优化/参数寻优Key words
Xgboost algorithm/model parameters/Bayesian optimization/parameter optimization分类
信息技术与安全科学引用本文复制引用
李叶紫,王振友,周怡璐,韩晓卓..基于贝叶斯最优化的Xgboost算法的改进及应用[J].广东工业大学学报,2018,35(1):23-28,6.基金项目
国家自然科学基金资助项目(11401115) （11401115）
广州市科技计划项目(201707010435) （201707010435）
