Skip to main content
SpringerLink
Log in

Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Nowadays, telecom industry faces fierce competition in satisfying its customers. This competition thus requires an efficient churn prediction system to identify customers who are ready to quit. Such churn customers are then retained through addressing relevant reasons identified by the churn prediction system. Therefore, now the role of churn prediction system is not only restricted to accurately predict churners but also to interpret customer churn behavior. In this paper, searching capabilities of genetic programming (GP) and classification capabilities of AdaBoost are integrated in order to evolve a high-performance churn prediction system having better churn identification abilities. For this, frequently selected features in various GP expressions evaluated through AdaBoost based learning, are marked and analyzed. Moreover, the issue of imbalance present in telecom datasets is also addressed through particle swarm optimization (PSO) based undersampling method, which provides unbiased distribution of training set to GP-AdaBoost based prediction system. Particle swarm optimization based undersampling method in combination with GP-AdaBoost results a churn prediction system (ChP-GPAB), which offers better learning of churners and also identifies underlying factors responsible for churn behavior of customers. Two standard telecom data sets are used for evaluation and comparison of the proposed ChP-GPAB system. The results show that the proposed ChP-GPAB system yields 0.91 AUC and 0.86 AUC on Cell2Cell and Orange datasets, in addition to identifying the reasons of churning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. ICT: The World in: ICT Facts and Figures, Geneva (2014)

  2. Shin, Y.H., David, C.Y., Hsiu, Y.W.: Applying data mining to telecom churn management. Expert Syst. Appl. 37, 3665–3675 (2006)

    Google Scholar 

  3. Bock, K.W.D., Van den Poel, D.: An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Syst. Appl. 38, 12293–12301 (2011)

    Article  Google Scholar 

  4. Huang, Y., Kechadi, T.: An effective hybrid learning system for telecommunication churn prediction. Expert Syst. Appl. 40, 5635–5647 (2013)

    Article  Google Scholar 

  5. Pendharkar, P.C.: Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services. Expert Syst. Appl. 36, 6714–6720 (2009)

    Article  Google Scholar 

  6. Burez, J., Van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36, 4626–4636 (2009)

    Article  Google Scholar 

  7. Mikel, G., Alberto, F., Edurne, B., Humberto, B., Francisco, H.: A review on ensembles for the class imbalance problem: bagging-boosting- and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. C 42, 463–484 (2012)

    Article  Google Scholar 

  8. Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B.: New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur. J. Oper. Res. 218, 211–229 (2012)

    Article  Google Scholar 

  9. Adnan, I., Muhammad, R., Asifullah, K.: Churn prediction in telecom using random forest and PSO based data balancing in combination with various feature selection strategies. Comput. Electr. Eng. 38, 1808–1819 (2012)

    Article  Google Scholar 

  10. Huang, B.Q., Kechadi, T.M., Buckley, B., Kiernan, G., Keogh, E., Rashid, T.: A new feature set with new window techniques for customer churn prediction in land-line telecommunications. Expert Syst. Appl. 37, 3657–3665 (2010)

    Article  Google Scholar 

  11. Huang, B., Buckley, B., Kechadi, T.M.: Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Syst. Appl. 37, 3638–3646 (2010)

    Article  Google Scholar 

  12. Kisioglu, P., Topcu, Y.I.: Applying Bayesian belief network approach to customer churn analysis: a case study on the telecom industry of Turkey. Expert Syst. Appl. 38, 7151–7157 (2011)

    Article  Google Scholar 

  13. Xu, H., Zhang, Z., Zhang, Y.: Churn prediction in telecom using a hybrid two-phase feature selection method. In: Third International Symposium on Intelligent Information Technology Application, 2009. IITA 2009, pp. 576–579 (2009)

  14. Owczarczuk, M.: Churn models for prepaid customers in the cellular telecommunication industry using large data marts. Expert Syst. Appl. 37, 4710–4712 (2010)

    Article  Google Scholar 

  15. Verbeke, W., Martens, D., Mues, C., Baesens, B.: Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38, 2354–2364 (2011)

    Article  Google Scholar 

  16. De Bock, K.W., Van den Poel, D.: Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. Expert Syst. Appl. 39, 6816–6826 (2012)

    Article  Google Scholar 

  17. Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. C 40, 121–144 (2010)

    Article  Google Scholar 

  18. Khan, G.M., Arshad, R., Mahmud, S.A., Ullah, F.: Intelligent bandwidth estimation for variable bit rate traffic. IEEE Trans. Evol. Comput. 19, 151–155 (2015)

    Article  Google Scholar 

  19. Bhowan, U., Johnston, M., Mengjie, Z., Xin, Y.: Reusing genetic programming for ensemble selection in classification of unbalanced data. IEEE Trans. Evol. Comput. 18, 893–908 (2014)

    Article  Google Scholar 

  20. Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2009)

    Article  Google Scholar 

  21. Sorokina, D.: Application of additive groves ensemble with multiple counts feature evaluation to KDD Cup ’09 small data set. In: Presented at the JMLR Workshop and Conference Proceedings, Paris (2009)

  22. Adnan, I., Asifullah, K., Lee, Y.S.: Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification. Appl. Intell. 39, 659–672 (2013)

    Article  Google Scholar 

  23. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., et al.: Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study. IEEE Access 4, 7940–7957 (2016)

    Article  Google Scholar 

  24. Yang, P., Xu, L., Zhou, B.B., Zhang, Z., Zomaya, A.Y.: A particle swarm based hybrid system for imbalanced medical data sampling. BMC Genom. 10, S34–S34 (2009)

    Article  Google Scholar 

  25. Au, W.H., Chan, K.C.C., Xin, Y.: A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Trans. Evol. Comput. 7, 532–545 (2003)

    Article  Google Scholar 

  26. Hadden, J., Tiwari, A., Roy, R., Ruta, D.: Computer assisted customer churn management: state-of-the-art and future trends. Comput. Oper. Res. 34, 2902–2917 (2007)

    Article  Google Scholar 

  27. Wang, P., Emmerich, M., Li, R., Tang, K., Back, T., Yao, X.: Convex Hull-based multiobjective genetic programming for maximizing receiver operating characteristic performance. IEEE Trans. Evol. Comput. 19, 12 (2015)

    Article  Google Scholar 

  28. Langdon, W.B., Harman, M.: Optimizing existing software with genetic programming. IEEE Trans. Evol. Comput. 19, 118–135 (2014)

    Article  Google Scholar 

  29. Lemmens, A., Croux, C.: Bagging and boosting classification trees to predict churn. J. Mark. Res. 43, 276–286 (2006)

    Article  Google Scholar 

  30. Bose, I., Chen, X.: Hybrid models using unsupervised clustering for prediction of customer churn. J. Org. Comput. Electron. Commer. 19, 133–151 (2009)

    Article  Google Scholar 

  31. Lima, E., Mues, C., Baesens, B.: Monitoring and backtesting churn models. Expert Syst. Appl. 38, 975–982 (2011)

    Article  Google Scholar 

  32. Chen, Z.-Y., Fan, Z.-P., Sun, M.: A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. Eur. J. Oper. Res. 223, 461–472 (2012)

    Article  MathSciNet  Google Scholar 

  33. Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., Abbasi, U.: Improved churn prediction in telecommunication industry using data mining techniques. Appl. Softw. Comput. 24, 994–1012 (2014)

    Article  Google Scholar 

  34. Kyoungok, K., Chi-Hyuk, J., Jaewook, L.: Improved churn prediction in telecommunication industry by analyzing a large network. Expert Syst. Appl. 41, 6575–6584 (2014)

    Article  Google Scholar 

  35. Ning, L., Hua, L., Jie, L., Guangquan, Z.: A customer churn prediction model in telecom industry using boosting. IEEE Trans. Industr. Inform. 10, 1659–1665 (2012)

    Google Scholar 

  36. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., et al.: Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing 237, 242–254 (2017)

    Article  Google Scholar 

  37. Amin, A., Khan, C., Ali, I., Anwar, S.: Customer churn prediction in telecommunication industry: with and without counter-example. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) Proceedings on Nature-Inspired Computation and Machine Learning: 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Tuxtla Gutiérrez, Mexico, November 16–22, 2014, Part II, pp. 206–218. Springer International Publishing, Cham (2014)

    Chapter  Google Scholar 

  38. Amin, A., Rahim, F., Ramzan, M., Anwar, S.: A prudent based approach for customer churn prediction. In: International Conference: Beyond Databases, Architectures and Structures, pp. 320–332 (2015)

    Chapter  Google Scholar 

  39. Miller, H., Clarke, S., Lane, S., Lonie, A., Lazaridiz, D., Petrovski, S., et al.: Predicting customer behaviour: the University of Melbourne’s KDD Cup report. In: Presented at the JMLR Workshop and Conference Proceedings, Paris (2009)

  40. Huang, B., Kechadi, M.T., Buckley, B.: Customer churn prediction in telecommunications. Expert Syst. Appl. 39, 1414–1425 (2012)

    Article  Google Scholar 

  41. Idris, A., Khan, A., Lee, Y.S.: Genetic programming and adaboosting based churn prediction for telecom. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1328–1332 (2012)

  42. Busa-Fekete, R., Kegl, B.: Accelerating AdaBoost using UCB. In: Presented at the JMLR Workshop and Conference Proceedings, Paris (2009)

Download references

Acknowledgements

This work is supported by the Higher Education Commission of Pakistan (HEC) as per Award No. 20-3408/R&D/HEC/14/233.

Author information

Authors and Affiliations

  1. Department of Computer Sciences & IT, The University of Poonch, Rawalakot, Pakistan

    Adnan Idris

  2. Department of Computer Sciences, COMSATS Institute of I.T, Lahore, Pakistan

    Aksam Iftikhar

  3. Department of Computer Sciences, COMSATS Institute of I.T, Abbottabad, Pakistan

    Zia ur Rehman

Authors
  1. Adnan Idris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aksam Iftikhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zia ur Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adnan Idris.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Idris, A., Iftikhar, A. & Rehman, Z.u. Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling. Cluster Comput 22 (Suppl 3), 7241–7255 (2019). https://doi.org/10.1007/s10586-017-1154-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1154-3

Keywords

Search

Navigation