Two-layer generalization boosting model for anomaly detection of e-commerce orders
Wenlong Wang, Huanyi Li, Tianyang Zhang, Zhaokun Wang, Yutao Lai · 2023
As e-commerce continues to grow in popularity, the study of abnormal data in e-commerce orders is increasingly important. By analyzing abnormal data in e-commerce orders, one can uncover problems and trends within the order data, ultimately leading to improved operational efficiency. However, many abnormal detection models face issues such as overfitting, excessive dependence on sample size, and the neglect of other important features. To address these issues, this study proposes a novel approach that combines the ensemble model with MLP. Specifically, this approach leverages the strengths of Random Forest, GBDT, and XGBoost, which serve as the first-layer feature extractors. Samples that are misclassified in the first layer are discarded, and the remaining training samples are input into the second-layer MLP. This approach enhances the model's generalization ability, strengthens its global modeling capability to a certain extent, and mitigates the problem of excessive dependence on samples. The dataset from Gitee validates that this two-layer model can effectively extract useful information contained in the data, and its accuracy surpasses that of classical ensemble models.