基于难案例挖掘的客户流失识别

Jianfeng Li, Xuepeng Bai, Qian Xu, Dexiang Yang
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引用次数: 0

摘要

在用户流失建模过程中,由于流失用户与留存用户之间的不平衡,使用传统的分类模型往往不能准确、全面地识别出具有流失倾向的用户。为了解决这一问题,仅仅增加成本敏感方法中少数类样本的误分类成本是不够的。本文提出了采用Focal Loss硬例挖掘技术,将类权值α和焦点参数γ加入到LightGBM的交叉熵损失函数中。此外,它强调了对有流失风险的客户的识别,并增加了对少数和难以分类的样本进行错误分类的成本。在上述思想的基础上,提出了FocalLoss_LightGBM模型,以及随机森林、SVM、XGBoost和LightGBM模型。基于Kaggle网站上公开的信用卡用户数据集的实证分析。AUC、TPR和G-mean指标值均优于现有模型,可有效提高潜在流失用户的准确性和稳定性。
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Identification of Customer Churn Considering Difficult Case Mining
In the process of user churn modeling, due to the imbalance between lost users and retained users, the use of traditional classification models often cannot accurately and comprehensively identify users with churn tendency. To address this issue, it is not sufficient to simply increase the misclassification cost of minority class samples in cost-sensitive methods. This paper proposes using the Focal Loss hard example mining technique to add the class weight α and the focus parameter γ to the cross-entropy loss function of LightGBM. In addition, it emphasizes the identification of customers at risk of churning and raises the cost of misclassification for minority and difficult-to-classify samples. On the basis of the preceding ideas, the FocalLoss_LightGBM model is proposed, along with random forests, SVM, XGBoost, and LightGBM. Empirical analysis based on a dataset of credit card users publicly available on the Kaggle website. The AUC, TPR, and G-mean index values were superior to the existing model, which can effectively improve the accuracy and stability of potential lost users.
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