基于在线序列优化极限学习机的电子商务客户流失量预测模型
|
摘要
为了提高电子商务客户流失量预测的准确性,针对单机处理模式无法有效预测海量电子商务客户流失量的难题,提出了在线序列优化极限学习机的电子商务客户流失量预测模型。首先通过云计算技术的Map/Reduce模式对电子商务客户流失量数据进行分割,得到多个训练子集,然后采用线序列优化极限学习机对电子商务客户流失量的每一个训练子集进行建模,并对训练子集的预测结果进行融合,得到电子商务客户流失量的最终预测结果,最后通过电子商务客户流失量算例进行模型的有效性进行验证。结果表明,该文模型提高了电子商务客户流失量的预测精度,而且减少了电子商务客户流失量建模的训练时间,大幅度提高了电子商务客户流失量预测速度。
In order to improve the customer churn prediction accuracy of E-commerce customer,and single machine model cannot effectively predict customer churn of massive E-commerce customers,this paper proposes a novel prediction model of customer churn in E-commerce based on online sequential optimization extreme learning machine. Firstly,the Map/Reduce model of cloud computing is used to segment the amount of customer churn in E-commerce,and multiple training subsets are obtained;secondly extreme learning machine is used to model each training subset of E-commerce customer churn,and the prediction results of training subsets are combined to get the final forecast results of customer churn in E-commerce;at last the validity of E-commerce customer churn prediction model is tested by example. The results show that the proposed model improves the prediction accuracy of customer churn in E-commerce,and the training time of E-commerce customer churn modeling has greatly reduced,improving the churn prediction speed of E-commerce customers.
In order to improve the customer churn prediction accuracy of E-commerce customer,and single machine model cannot effectively predict customer churn of massive E-commerce customers,this paper proposes a novel prediction model of customer churn in E-commerce based on online sequential optimization extreme learning machine. Firstly,the Map/Reduce model of cloud computing is used to segment the amount of customer churn in E-commerce,and multiple training subsets are obtained;secondly extreme learning machine is used to model each training subset of E-commerce customer churn,and the prediction results of training subsets are combined to get the final forecast results of customer churn in E-commerce;at last the validity of E-commerce customer churn prediction model is tested by example. The results show that the proposed model improves the prediction accuracy of customer churn in E-commerce,and the training time of E-commerce customer churn modeling has greatly reduced,improving the churn prediction speed of E-commerce customers.
引文
[1] 于小兵,曹杰,巩在武. 客户流失问题研究综述[J]. 计算机集成制造系统,2012,18(10):2253-2263.Yu Xiaobing,Cao Jie,Gong Zaiwu. Review on customer churn issue[J]. Computer Integrated Manufacturing Systems,2012,18(10):2253-2263.
[2] 余乐安. 基于最小二乘近似支持向量回归模型的电子商务信用风险预警[J]. 系统工程理论与实践,2012,32(3):508-514.Yu Lean. E-commerce credit risk early-warning with a least squares proximal support vector regression model[J]. Systems Engineering Theory & Practice,2012,32(3):508-514.
[3] Re N,Jit H S. An integrated framework to recommend personalized retention actions to control B2C E-commerce customer churn[J]. International Journal of Engineering Trends and Technology,2015,53(3):152-157.
[4] 琚春华,卢琦蓓,郭飞鹏. 融入个体活跃度的电子商务客户流失预测模型[J]. 系统工程理论与实践,2013,33(1):141-150.Ju Chunhua,Lu Qibei,Guo Feipen. E-commerce customer churn prediction model combined with individual activity[J]. Systems Engineering Theory & Practice,2013,33(1):141—150.
[5] 张秋菊,朱帮助. 基于自组织数据挖掘的电子商务客户流失预测模型[J]. 企业经济,2011,22(1):95-99.Zhang QiuJu,Zhu Bangzhu. Churn prediction model of E-business customer based on self-organized data mining[J]. Enterprise Economy,2011,22(1):95-99.
[6] 罗彬,邵培基,罗尽尧,等. 基于粗糙集理论-神经网络-蜂群算法集成的客户流失研究[J]. 管理学报,2011,8(2):265-272.Luo Bin,Shao Peiji,Luo Jinyao,et al. Customer churn research based on multiple classifier fusing rough sets-neural network-artificial bee colony algorithm[J]. Chinese Journal of Management,2011,8(2):265-272.
[7] 朱帮助. 基于SMC-RS-LSSVM的电子商务客户流失预测模型[J]. 系统工程理论与实践,2010,30(11):1960-1967.Zhu Bangzhu. E-business customer churn prediction based on integration of SMC,rough sets and least square support vector machine[J]. Systems Engineering Theory & Practice,2010,30(11):1960-1967.
[8] 代逸生,沈培兰,孙红霞. 基于Pareto/NBD模型的电子商务网站客户流失预测研究[J]. 科学技术与工程,2010,27(10):6792-6795.Dai Yisheng,Shen Peilan,Sun Hongxia. Research for E-commerce customer churns based on Pareto/NBD Model[J]. Science Technology and Engineering,2010,27(10):6792-6795.
[9] BahmaniB,Mohammadi G,Mohammadi M,et al. Customer churn prediction by hybrid neural networks[J]. Expert Systems with Applications,2009,36(10):12547-12553.
[10] 朱帮助,张秋菊,邹昊飞,等. 基于OSA算法和GMDH网络集成的电子商务客户流失预测[J]. 中国管理科学,2011,19(5):64-70.Zhu Bangzhu,Zhang Qiuju,Zou Haofei,et al. E-business customer churn prediction based on integration of objective system analysis and group method of data handling network[J]. Chinese Journal of Management Science,2011,l9(5):64-70
[11] 夏国恩,邵培基. 改进的支持向量分类机在客户流失估计中的应用[J]. 计算机应用研究,2009,26(6):2044-2046.Xia Guoen,Shao Peiji. Application of improved support vector classifier in customer churn prediction[J]. Application Research of Computers,2009,26(6):2044-2046.
[12] Chen Zhenyu,Fan Zhiping,Sun Minghe. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data[J]. European Journal of Operational Research,2012,223(2):461-472.
[13] 武小军,孟苏芳. 基于客户细分和AdaBoost的电子商务客户流失预测研究[J]. 工业工程,2017,20(2):99-107.Wu Xiaojun,Meng Sufang. E-commerce customer churn prediction based on customer segmentation and AdaBoost[J]. Industrial Engineering Journal,2017,20(2):99-107.
[14] 黄纬,温志萍,程初. 云计算中基于K-均值聚类的虚拟机调度算法研究[J]. 南京理工大学学报,2013,37(6):807-812.Huang Wei,Wen Zhiping,Cheng Chu. Virtual machine scheduling algorithm based on K-means clustering in cloud computing[J]. Journal of Nanjing University of Science and Technology,2013,37(6):807-812.
[15] 刘念,张清鑫,李小芳. 基于核函数极限学习机的分布式光伏短期功率预测[J]. 农业工程学报,2014,30(4):152-159.Liu Nian,Zhang Qingxin,Li Xiaofang. Distributed photovoltaic short-term power output forecasting based on extreme learning machine with kernel[J]. Transactions of the Chinese Society of Agricultural Engineering,2014,30(4):152-159.
[2] 余乐安. 基于最小二乘近似支持向量回归模型的电子商务信用风险预警[J]. 系统工程理论与实践,2012,32(3):508-514.Yu Lean. E-commerce credit risk early-warning with a least squares proximal support vector regression model[J]. Systems Engineering Theory & Practice,2012,32(3):508-514.
[3] Re N,Jit H S. An integrated framework to recommend personalized retention actions to control B2C E-commerce customer churn[J]. International Journal of Engineering Trends and Technology,2015,53(3):152-157.
[4] 琚春华,卢琦蓓,郭飞鹏. 融入个体活跃度的电子商务客户流失预测模型[J]. 系统工程理论与实践,2013,33(1):141-150.Ju Chunhua,Lu Qibei,Guo Feipen. E-commerce customer churn prediction model combined with individual activity[J]. Systems Engineering Theory & Practice,2013,33(1):141—150.
[5] 张秋菊,朱帮助. 基于自组织数据挖掘的电子商务客户流失预测模型[J]. 企业经济,2011,22(1):95-99.Zhang QiuJu,Zhu Bangzhu. Churn prediction model of E-business customer based on self-organized data mining[J]. Enterprise Economy,2011,22(1):95-99.
[6] 罗彬,邵培基,罗尽尧,等. 基于粗糙集理论-神经网络-蜂群算法集成的客户流失研究[J]. 管理学报,2011,8(2):265-272.Luo Bin,Shao Peiji,Luo Jinyao,et al. Customer churn research based on multiple classifier fusing rough sets-neural network-artificial bee colony algorithm[J]. Chinese Journal of Management,2011,8(2):265-272.
[7] 朱帮助. 基于SMC-RS-LSSVM的电子商务客户流失预测模型[J]. 系统工程理论与实践,2010,30(11):1960-1967.Zhu Bangzhu. E-business customer churn prediction based on integration of SMC,rough sets and least square support vector machine[J]. Systems Engineering Theory & Practice,2010,30(11):1960-1967.
[8] 代逸生,沈培兰,孙红霞. 基于Pareto/NBD模型的电子商务网站客户流失预测研究[J]. 科学技术与工程,2010,27(10):6792-6795.Dai Yisheng,Shen Peilan,Sun Hongxia. Research for E-commerce customer churns based on Pareto/NBD Model[J]. Science Technology and Engineering,2010,27(10):6792-6795.
[9] BahmaniB,Mohammadi G,Mohammadi M,et al. Customer churn prediction by hybrid neural networks[J]. Expert Systems with Applications,2009,36(10):12547-12553.
[10] 朱帮助,张秋菊,邹昊飞,等. 基于OSA算法和GMDH网络集成的电子商务客户流失预测[J]. 中国管理科学,2011,19(5):64-70.Zhu Bangzhu,Zhang Qiuju,Zou Haofei,et al. E-business customer churn prediction based on integration of objective system analysis and group method of data handling network[J]. Chinese Journal of Management Science,2011,l9(5):64-70
[11] 夏国恩,邵培基. 改进的支持向量分类机在客户流失估计中的应用[J]. 计算机应用研究,2009,26(6):2044-2046.Xia Guoen,Shao Peiji. Application of improved support vector classifier in customer churn prediction[J]. Application Research of Computers,2009,26(6):2044-2046.
[12] Chen Zhenyu,Fan Zhiping,Sun Minghe. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data[J]. European Journal of Operational Research,2012,223(2):461-472.
[13] 武小军,孟苏芳. 基于客户细分和AdaBoost的电子商务客户流失预测研究[J]. 工业工程,2017,20(2):99-107.Wu Xiaojun,Meng Sufang. E-commerce customer churn prediction based on customer segmentation and AdaBoost[J]. Industrial Engineering Journal,2017,20(2):99-107.
[14] 黄纬,温志萍,程初. 云计算中基于K-均值聚类的虚拟机调度算法研究[J]. 南京理工大学学报,2013,37(6):807-812.Huang Wei,Wen Zhiping,Cheng Chu. Virtual machine scheduling algorithm based on K-means clustering in cloud computing[J]. Journal of Nanjing University of Science and Technology,2013,37(6):807-812.
[15] 刘念,张清鑫,李小芳. 基于核函数极限学习机的分布式光伏短期功率预测[J]. 农业工程学报,2014,30(4):152-159.Liu Nian,Zhang Qingxin,Li Xiaofang. Distributed photovoltaic short-term power output forecasting based on extreme learning machine with kernel[J]. Transactions of the Chinese Society of Agricultural Engineering,2014,30(4):152-159.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |