计算机应用与软件2024，Vol.41Issue(1)：12-17,35,7.DOI:10.3969/j.issn.1000-386x.2024.01.003
基于三重混合采样和集成学习的潜在高价值旅客发现
POTENTIAL HIGH-VALUE PASSENGER DISCOVERY BASED ON SSOMAJ-SMOTE-SSOMIN SAMPLING AND ENSEMBLE LEARNING
摘要
Abstract
Considering highly-imbalanced data and weak correlation between passenger characteristics and value categories of potential high-value passenger,a potential high-value passenger discovery model based on SSOMaj-SMOTE-SSOMin sampling and ensemble learning is proposed.The RFM method was used to label the passenger category.The SSOMaj-SMOTE-SSOMin method was used to resample the imbalanced passenger data set.The fusion feature selection algorithm(FFS)was used to select the passenger features.Gradient boosting decision tree(GBDT)was taken as the classifier to build a passenger value prediction model to identify potential high-value passengers.Compared with the baseline algorithm,the experimental results on the PNR data set show that the proposed model achieves better AUC value and F1 value,and can better identify potential high-value passengers.关键词
航空运输/三重混合采样/特征重要性排序/潜在高价值旅客/不平衡分类/集成学习Key words
Air transportation/SSOMaj-SMOTE-SSOMin/Feature importance ranking/Potential high value passenger/Imbalanced classification/Ensemble learning分类
计算机与自动化引用本文复制引用
冯霞,胡昉..基于三重混合采样和集成学习的潜在高价值旅客发现[J].计算机应用与软件,2024,41(1):12-17,35,7.基金项目
国家自然科学基金项目(61502499) （61502499）
中国民航大学科研基金项目(2013QD18X) （2013QD18X）
民航旅客服务智能化应用技术重点实验室项目. （）
