基于CART和自适应Boosting算法的移动通信企业客户流失预测模型

Abstract

Abstract: Customer churn problems have been taking seriously by Enterprises, and how to predict churning customers effectively has been becoming an important subject. Firstly the original data of a mobile communication enterprise is preprocessed strictly, and the histogram test and the chi-square test are employed for choosing variables for the prediction model. Then a sampling method is applied to extract data for training and testing, and a strong classifier model based on Classification and Regression Tree and adaptive boosting algorithm is constructed by using training samples. At last, a simulation experiment is adopted and the results of the experiment show that the integrated model used in this paper had high prediction precision. The ROC curve presented in the paper also illustrates the model is an ideal classification model. Meanwhile, the model has been proved to have better prediction performance by comparison with the other two models.

Key words: customer churning, adaptive boosting algorithm, CART algorithm, prediction

CLC Number:

TP391

ZHANG Wei, YANG Shan-lin, LIU Ting-ting. Customer Churn Prediction in Mobile Communication Enterprises based on CART and Boosting Algorithm[J]. Chinese Journal of Management Science, 2014, 22(10): 90-96.

References

[1] Buckinx W, Van den Poel D. Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting [J]. European Journal of Operational Research, 2005, 164(1): 252-268.

[2] Verbeke W, Dejaeger K, Martens D, et al. New insights into churn prediction in the telecommunication sector: A profit driven data mining approach [J]. European Journal of Operational Research, 2012, 218(1): 211-229.

[3] Glady N, Baesens B, Croux C. Modeling churn using customer lifetime value [J]. European Journal of Operational Research, 2009, 197(1): 402-411.

[4] Tsai C, Lu Y. Customer churn prediction by hybrid neural networks [J]. Expert Systems with Applications, 2009, 36(10): 12547-12553.

[5] 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.

[6] 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.

[7] Kisioglu P, Topcu Y. Applying Bayesian belief network approach to customer churn analysis: A case study on the telecom industry of Turkey [J]. Expert Systems with Applications, 2011, 38: 7151-7157.

[8] 罗彬,邵培基,罗尽尧, 等. 基于蚁群算法的成本敏感线性集成多分类器的客户流失研究 [J]. 中国管理科学,2010,18(3):58-67.

[9] 罗彬,邵培基,罗尽尧, 等. 基于多分类器动态集成的电信客户流失预测 [J]. 系统工程学报,2010,25(5):703-711.

[10] 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.

[11] 夏国恩,金炜东. 基于支持向量机的客户流失预测模型 [J]. 系统工程理论与实践,2008,1(1):71-77.

[12] Huang Bingquan, Kechadi T, Buckley B, et al. A new feature set with new window techniques for customer churn prediction in land-line telecommunications [J]. Expert Systems with Applications, 2010, 37(5): 3657-3665.

[13] 应维云,覃正,赵宇, 等. SVM方法及其在客户流失预测中的应用研究 [J]. 系统工程理论与实践,2007, 27(7):105-110.

[14] 蒙肖莲,蔡淑琴,杜宽旗,等. 行业银行客户流失预测模型研究 [J]. 系统工程,2004,22(12):67-71.

[15] 罗彬,邵培基,罗尽尧, 等. 基于竞争对手反击的电信客户流失挽留研究 [J]. 管理科学学报,2011,14(8):17-33.

[16] 胡理增,陈建军. 无约束条件下多客户流失挽救最优化决策 [J]. 中国管理科学,2009,17(6):39-43.

[17] 胡理增,于信阳,张长赋,等. 基于经费约束和广义客户终身价值最大化的多客户流失挽救模型 [J]. 系统工程理论与实践,2009,29(2):63-69.

[18] Mozer M, Wolniewicz R, Grimes D, et al. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry [J]. IEEE Transactions on Neural Networks, 2000, 11(3): 690-696.

[19] Hwang H, Jung T, Suh E. An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry [J]. Expert Systems with Applications, 2004, 34(1): 231-241.

[20] Marsland S. Machine Learning [M]. Florida: Chapman & Hall/CRC, 2009.

[21] Valiant L G. A theory of the learnable [J]. Communications of the ACM, 1984, 27(11): 1134-1142.

[22] Schapire R. The strength of weak learnability [J]. Machine Learning, 1990, 5(2): 197-227.

[23] Freund Y. Boosting a weak learning algorithm by majority [J]. Information and Computation, 1995, 121(2): 256-285.

[24] Freund Y, Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting [J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139.

[25] Freund Y, Schapire R. Experiments with a new boosting algorithm [J]. Machine Learning, 1996, 96:148-156.

[26] Schapire R, Singer Y. Improved boosting algorithms using confidence-rated predictions [J]. Machine Learning, 1999,37(3):297-336.

Customer Churn Prediction in Mobile Communication Enterprises based on CART and Boosting Algorithm

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Hongxu Yan,Shunkun Yu. Research on Employee Turnover Prediction Model Based on the Portrait [J]. Chinese Journal of Management Science, 2024, 32(9): 303-312.
[2]	Shuliang Li,Shuangyi Yang,Bo Zeng,Wei Meng,Yun Bai. Unbiased and Adaptive of Discrete Grey Prediction Model and Its Application [J]. Chinese Journal of Management Science, 2024, 32(8): 149-158.
[3]	Xiaojian Yu,Guopeng Liu,Jianlin Liu,Weilin Xiao. Stock Index Prediction Based on LSTM Network and Text Sentiment Analysis [J]. Chinese Journal of Management Science, 2024, 32(8): 25-35.
[4]	Aizhong Li,Ruoen Ren,Jichang Dong. Graph Network Risk Perception and Sparse Low-rank Portfolio Management Strategy [J]. Chinese Journal of Management Science, 2024, 32(4): 58-65.
[5]	Long Shen,Ying Zhou. Credit Scoring Model Based on JS Divergence Feature Discretization [J]. Chinese Journal of Management Science, 2024, 32(10): 41-55.
[6]	Dawen Yan,Cun Li,Guotai Chi. Dynamic Financial Distress Prediction for Chinese Listed Companies Based on the Mixed Frequency Data [J]. Chinese Journal of Management Science, 2024, 32(1): 1-12.
[7]	Lianyi Liu,Sifeng Liu,Lifeng Wu. New Energy Vehicle Sales Forecast Based on Siscrete Time Grey Power Model [J]. Chinese Journal of Management Science, 2024, 32(1): 106-114.
[8]	Jiang-tao WANG,Ya CAI,Cheng-li ZHENG. An Adaptive Algorithm for Prediction of Risk and Its Application [J]. Chinese Journal of Management Science, 2023, 31(8): 1-8.
[9]	LIU Jin-pei, LUO Rui, CHEN Hua-you, TAO Zhi-fu. Multi-scale Combination Forecasting of Interval Exchange Rate with Hybrid Secondary Decomposition Driven by Unstructured Data [J]. Chinese Journal of Management Science, 2023, 31(6): 60-70.
[10]	YI Jing-tao, YAN Huan. Early Prediction and Warning of International Trade Risks Based on Wavelet Decomposition and ARIMA-GRU Hybrid Model [J]. Chinese Journal of Management Science, 2023, 31(6): 100-110.
[11]	HU Jin-song, LIU Yu-hong, MA De-qing. Research on Food Supply Chain Dynamic Strategy Formulation and Coordination under the Prediction of Food Safety Crisis [J]. Chinese Journal of Management Science, 2023, 31(5): 116-131.
[12]	DONG Bing-jie, CHI Guo-tai. Study on Default Prediction Based on Sentiment Data [J]. Chinese Journal of Management Science, 2023, 31(4): 111-120.
[13]	OUYANG Lin-han, TAO Bao-ping, MA Yan. Research on Quality Prediction Based on Gaussian Process Model with Selective Ensemble Kernel [J]. Chinese Journal of Management Science, 2023, 31(3): 69-80.
[14]	GAO Ming, LIU Chao, TANG Jia-fu, SUN Si-jing, ZOU Guang-yu. Lifetime Decay Prediction of Fuel Cell Based on Attention Neural Network [J]. Chinese Journal of Management Science, 2023, 31(3): 155-166.
[15]	Peng ZHANG,Shi-li DANG,Mei-yu HUANG,Jing-xin LI. Two-stage Mean Semi-variance Portfolio Optimization with Stock Return Prediction Using Machine Learning [J]. Chinese Journal of Management Science, 2023, 31(12): 96-106.