Advertisement

Cluster Computing

, Volume 22, Supplement 3, pp 7241–7255 | Cite as

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

  • Adnan IdrisEmail author
  • Aksam Iftikhar
  • Zia ur Rehman
Article

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.

Keywords

Telecom Churn prediction Genetic programming AdaBoost Ensemble classification Feature identification 

Notes

Acknowledgements

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

References

  1. 1.
    ICT: The World in: ICT Facts and Figures, Geneva (2014)Google Scholar
  2. 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. 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)CrossRefGoogle Scholar
  4. 4.
    Huang, Y., Kechadi, T.: An effective hybrid learning system for telecommunication churn prediction. Expert Syst. Appl. 40, 5635–5647 (2013)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Burez, J., Van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36, 4626–4636 (2009)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)Google Scholar
  14. 14.
    Owczarczuk, M.: Churn models for prepaid customers in the cellular telecommunication industry using large data marts. Expert Syst. Appl. 37, 4710–4712 (2010)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 20.
    Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2009)CrossRefGoogle Scholar
  21. 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)Google Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 28.
    Langdon, W.B., Harman, M.: Optimizing existing software with genetic programming. IEEE Trans. Evol. Comput. 19, 118–135 (2014)CrossRefGoogle Scholar
  29. 29.
    Lemmens, A., Croux, C.: Bagging and boosting classification trees to predict churn. J. Mark. Res. 43, 276–286 (2006)CrossRefGoogle Scholar
  30. 30.
    Bose, I., Chen, X.: Hybrid models using unsupervised clustering for prediction of customer churn. J. Org. Comput. Electron. Commer. 19, 133–151 (2009)CrossRefGoogle Scholar
  31. 31.
    Lima, E., Mues, C., Baesens, B.: Monitoring and backtesting churn models. Expert Syst. Appl. 38, 975–982 (2011)CrossRefGoogle Scholar
  32. 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)MathSciNetCrossRefGoogle Scholar
  33. 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)CrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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. 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)CrossRefGoogle Scholar
  37. 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)CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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)Google Scholar
  40. 40.
    Huang, B., Kechadi, M.T., Buckley, B.: Customer churn prediction in telecommunications. Expert Syst. Appl. 39, 1414–1425 (2012)CrossRefGoogle Scholar
  41. 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)Google Scholar
  42. 42.
    Busa-Fekete, R., Kegl, B.: Accelerating AdaBoost using UCB. In: Presented at the JMLR Workshop and Conference Proceedings, Paris (2009)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Sciences & ITThe University of PoonchRawalakotPakistan
  2. 2.Department of Computer SciencesCOMSATS Institute of I.TLahorePakistan
  3. 3.Department of Computer SciencesCOMSATS Institute of I.TAbbottabadPakistan

Personalised recommendations