[1]利向晴,夏国恩,张显全,等.基于深度神经网络权重集成的客户流失预测[J].计算机技术与发展,2021,31(10):18-23.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 004]
　LI Xiang-qing,XIA Guo-en,ZHANG Xian-quan,et al.Customer Churn Prediction Based on Deep Neural Network Weight Ensemble[J].,2021,31(10):18-23.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 004]
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基于深度神经网络权重集成的客户流失预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年10期
页码:
18-23
栏目:
大数据分析与挖掘
出版日期:
2021-10-10

文章信息/Info

Title:
Customer Churn Prediction Based on Deep Neural Network Weight Ensemble
文章编号:
1673-629X(2021)10-0018-06
作者:
利向晴1 ; 夏国恩1; 2 ; 张显全1 ; 唐 琪1 ; 叶 帅1
1. 广西师范大学 计算机科学与信息工程学院,广西 桂林 541004;
2. 广西财经学院 工商管理学院,广西 南宁 530003
Author(s):
LI Xiang-qing1 ; XIA Guo-en1; 2 ; ZHANG Xian-quan1 ; TANG Qi1 ; YE Shuai1
1. School of Computer Science and Information Engineering,Guangxi Normal University,Guilin 541004,China;
2. School of Business Administration,Guangxi University of Finance and Economics,Nanning 530003,China
关键词:
客户流失; 深度学习; 深度神经网络; 随机加权平均; 权重集成
Keywords:
customer churn; deep learning; deep neural network; stochastic weight average; weighted ensemble
分类号:
TP183
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 10. 004
摘要:
如今许多的企业都面临着客户流失的实质问题,客户流失预测对企业的发展也是尤为重要的。 对于客户流失预测的问题也有很多解决方案,传统的机器学习方法存在特征工程对模型效果影响较大的缺点,而深度学习则使得算法不会太依赖于领域专业知识和人工特征提取。 为提升预测效果,提出一种深度神经网络权重集成方法来对电信客户流失进行预测,其主要思想是在做深度神经网络( DNN) 训练的时候,通过随机加权平均( stochastic weight average,SWA) 结合同一网络结构下的不同训练阶段的权重获取集成模型,然后对客户流失进行预测。 实验结果表明,相比于深度神经网络训练,结合随机加权平均权重集成的深度神经网络训练时间缩短了 2. 96 倍,同时,准确率、精准率、召回率和 F1 值均有所提升。
Abstract:
Now many enterprises are faced with the problem of customer churn,and customer churn prediction is particularly important for the development of enterprises. There are many solutions for customer churn prediction. Traditional machine learning methods have the disadvantage that feature engineering has great influence on model effect, and deep learning makes the algorithm less dependent on domain expertise and artificial feature extraction. In order to improve the prediction effect, a deep neural network combined with stochastic weights average is proposed to predict customer churn. The main idea is to obtain the integration model by combining the stochastic weight average ( SWA) with the weights of different training stages under the same network structure when doing DNN training,and then predict customer churn. The experiment shows that compared with the deep neural network training,the training time of the deep neural network combined with the stochastic weighted average weight integration is shortened by 2. 96 times,and the accuracy,precision,recall and F1 are improved.

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更新日期/Last Update: 2021-10-10