基于三重混合采样和集成学习的潜在高价值旅客发现OACSTPCD

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.