In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction method and model fusion technology are proposed to solve the problem of basic medical insurance fraud identification. The feature second-level extraction algorithm proposed in this paper can effectively extract important features and improve the prediction accuracy of subsequent algorithms. In order to solve the problem of unbalanced simulation allocation in the medical insurance fraud identification scenario, a sample division method based on the idea of sample proportion equilibrium is proposed. Based on the above methods of feature extraction and sample division, a new training and fitting model fusion algorithm (tree hybrid bagging, THBagging) is proposed. This method makes full use of the balanced idea of the tree model algorithm based on Boosting to fuse, and finally achieves the effect of improving the accuracy of basic medical insurance fraud identification.

中文翻译：

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基于组合分类器融合（THBagging）的综合学习欺诈检测方法研究：以基础医疗保险数据集为例

近年来，基本医疗保险中的欺诈案件数量急剧增加。我们需要使用一种更有效的方法来识别欺诈用户。因此，我们以较低的延迟部署云边缘算法，以提高操作过程中的安全性和可执行性。针对基本医疗保险欺诈识别问题，提出了一种新的特征提取方法和模型融合技术。本文提出的特征二级提取算法可以有效地提取重要特征，提高后续算法的预测精度。为了解决医疗保险欺诈识别场景中模拟分配不均衡的问题，提出了一种基于样本比例均衡思想的样本分割方法。基于上述特征提取和样本划分方法，提出了一种新的训练和拟合模型融合算法（树混合袋装，THBagging）。该方法充分利用了基于Boosting的树模型算法的平衡思想进行融合，最终达到提高基本医疗保险欺诈识别准确性的效果。