[1]马文斌,夏国恩.基于深度神经网络的客户流失预测模型[J].计算机技术与发展,2019,29(09):76-80.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 015]
　MA Wen-bin,XIA Guo-en.Customer Churn Prediction Model Based on Deep Neural Network[J].,2019,29(09):76-80.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 015]
点击复制

基于深度神经网络的客户流失预测模型()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年09期
页码:
76-80
栏目:
智能、算法、系统工程
出版日期:
2019-09-10

文章信息/Info

Title:
Customer Churn Prediction Model Based on Deep Neural Network
文章编号:
1673-629X(2019)09-0076-05
作者:
马文斌; 夏国恩
广西财经学院 工商管理学院,广西 南宁 530003
Author(s):
MA Wen-bin; XIA Guo-en
School of Business Administration,Guangxi University of Finance and Economics,Nanning 530003,China
关键词:
深度学习; 深度神经网络; 客户流失; 电信
Keywords:
deep learning; deep neural network; customer churn; telecommunications
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 09. 015
摘要:
客户流失是企业面临的一个重要问题,为及时发现流失客户,降低企业损失,目前已有许多研究对客户流失问题给出解决方案,但是大部分研究中使用的是浅层学习算法,预测结果依赖于特征选择,需要在特征工程上花费大量的时间和精力。 随着客户数据的快速增长,在大数据情况下,人工特征工程已不能有效地获取高质量特征。 深度学习通过模拟人脑多层、逐级地抽取信息特征,能自动学习到较好的数据特征,在图像识别、语音识别等领域取得显著成果。 为研究深度学习在客户流失预测方面的应用,构造了基于深度神经网络的流失预测模型,并在电信客户数据集上,与经过特征选择的 Logistic 回归、决策树等预测模型作对比,验证其预测准确度。 实验结果表明,深度神经网络模型取得了较好的预测效果。
Abstract:
One of the important problem enterprise faced is customer churn. In order to find out the customer loss in time and reduce the loss of enterprises,many researchers have proposed solutions to the problem of customer churn. However,most studies use shallow learning algorithm,whose prediction results depend on feature selection and require a lot of time and energy in feature engineering. With the rapid growth of customer data,in the case of big data,artificial feature engineering has been unable to effectively obtain high-quality features. Deep learning can automatically learn better data features by simulating the human brain to extract information features in multiple layers and step by step,making remarkable achievements in the fields of image recognition and speech recognition. In order to study the application of deep learning in customer churn prediction,a churn prediction model based on deep neural network is constructedand compared with the Logistic regression,decision tree and other models after feature selection in the telecom customer data set to test the prediction accuracy. Experiment shows that deep neural network model has better prediction effect.

相似文献/References:

[1]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].,2016,26(09):19.
[2]贺飞翔,赵启军. 基于深度学习的头部姿态估计[J].计算机技术与发展,2016,26(11):1.
　HE Fei-xiang,ZHAO Qi-jun. Head Pose Estimation Based on Deep Learning[J].,2016,26(09):1.
[3]曾志平[] [],萧海东[],张新鹏[]. 基于DBN的金融时序数据建模与决策[J].计算机技术与发展,2017,27(04):1.
　ZENG Zhi-ping[] [],XIAO Hai-dong[],ZHANG Xin-peng[]. Modeling and Decision-making of Financial Time Series Data with DBN[J].,2017,27(09):1.
[4]林舒都,邵曦.基于i-vector和深度学习的说话人识别[J].计算机技术与发展,2017,27(06):66.
　LIN Shu-du,SHAO Xi. Speaker Recognition with i-vector and Deep Learning[J].,2017,27(09):66.
[5]李宏林. 分析式纹理合成技术及其在深度学习的应用[J].计算机技术与发展,2017,27(11):7.
　LI Hong-lin. Analyzed Texture-synthesis Techniques and Their Applications in Deep Learning[J].,2017,27(09):7.
[6]曲之琳,胡晓飞.基于改进激活函数的卷积神经网络研究[J].计算机技术与发展,2017,27(12):77.[doi:10.3969/ j. issn.1673-629X.2017.12.017]
　QU Zhi-lin,HU Xiao-fei.Research on Convolutional Neural Network Based on Improved Activation Function[J].,2017,27(09):77.[doi:10.3969/ j. issn.1673-629X.2017.12.017]
[7]吴 超,邵 曦.基于深度学习的指静脉识别研究[J].计算机技术与发展,2018,28(02):200.[doi:10．3969/j．issn．1673－629X．2018．02．043]
　WU Chao,SHAO Xi.Research on Finger Vein Ｒecognition Based on Deep Learning[J].,2018,28(09):200.[doi:10．3969/j．issn．1673－629X．2018．02．043]
[8]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(09):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[9]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].,2018,28(09):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
[10]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(09):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
[11]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.[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(09):18.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 004]
[12]李 林,俞 璐,韩昌芝,等.多源域适应方法综述[J].计算机技术与发展,2024,34(03):1.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 001]
　LI Lin,YU Lu,HAN Chang-zhi,et al.A Review of Multi-source Domain Adaptation[J].,2024,34(09):1.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 001]

更新日期/Last Update: 2019-09-10