数据与计算发展前沿2024，Vol.6Issue(5)：126-138,13.DOI:10.11871/jfdc.issn.2096-742X.2024.05.012
基于二重LOF与逆交叉验证的稳健AdaBoost回归模型
Robust AdaBoost Regression Model Based on Double LOF and Inverse-Cross-Validation
摘要
Abstract
[Objective]The robustness of the traditional AdaBoost regression model is insufficient.The improved AdaBoost.RT+and AdaBoost.RS algorithms hold insignificant suppression on abnormal data and low identification accuracy of abnormal data.It is meaningful to enhance the robustness of AdaBoost algorithms.[Methods]First,dual LOF and inverse cross valida-tion algorithms are proposed,the abnormal degree of data is characterized by probability based on these two algorithms.Then,appropriate weight coefficients are given according to the abnormal degree of the data to suppress its influence and keep no effect on the normal data.[Results]This AdaBoost.R_LOF model holds better robustness and less mean squared error on prediction.[Limitations]However,more hyperparameters are needed.[Conclusions]Simulations and real appli-cations show that the new model has better robustness and estimation under the different proportions of outliers compared with AdaBoost.R2,AdaBoost.RT+and AdaBoost.RS algorithms.关键词
AdaBoost算法/二重LOF算法/逆交叉验证/AdaBoost.R_LOF算法Key words
oAdaBoost/double LOF/Inverse-Cross-Validation/AdaBoost.R_LOF引用本文复制引用
曾凡倍,杨联强..基于二重LOF与逆交叉验证的稳健AdaBoost回归模型[J].数据与计算发展前沿,2024,6(5):126-138,13.基金项目
安徽高校自然科学基金(KJ2021A0049) （KJ2021A0049）
安徽省自然科学基金(2208085MA06) （2208085MA06）
