High accuracy predictive modelling for customer churn prediction in telecom industry

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10358 LNAI, Issue , 2017, Pages 391-402

  Prashanth, R ^a   Deepak, K ^a   Meher, Amit Kumar ^a  

^a R and D Department Flytxt Mobile Solutions Pvt Ltd   (India)

Author keywords

Churn prediction; Data mining; Deep learning; Statistical testing

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION TREES; DEEP NEURAL NETWORKS; EDUCATION; FORECASTING; LEARNING SYSTEMS; PATTERN RECOGNITION; PUBLIC RELATIONS; RECURRENT NEURAL NETWORKS; SALES;

CHURN PREDICTIONS; CONVENTIONAL MACHINES; CUSTOMER CHURN PREDICTION; CUSTOMER MANAGEMENTS; CUSTOMER RELATIONSHIP MANAGEMENT; LEARNING ARCHITECTURES; PREDICTIVE MODELLING; STATISTICAL TESTING;

DEEP LEARNING;

EID: 85025115500     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-62416-7_28     Document Type: Conference Paper

References (15)

1. Chen, Z.Y., Zhen-Yu, C., Zhi-Ping, F., Minghe, S.: A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. Eur. J. Oper. Res. 223(2), 461–472 (2012)
2. Olle, G., Georges, O.: A hybrid churn prediction model in mobile telecommunication industry. Int. J. e-Educ. e-Bus. e-Manag. e-Learn. 4(1), 55 (2014)
3. Brandusoiu, I., Ionut, B., Gavril, T., Horia, B.: Methods for churn prediction in the pre-paid mobile telecommunications industry. In: International Conference on Communications (COMM) (2016)
4. Lu, N., Ning, L., Hua, L., Jie, L., Guangquan, Z.: A customer churn prediction model in telecom industry using boosting. IEEE Trans. Ind. Inf. 10(2), 1659–1665 (2014)
5. Ismail, M.R., Awang, M.K., Rahman, M.N.A., Mokhairi, M.: A multi-layer perceptron approach for customer churn prediction. Int. J. Multimed. Ubiquitous Eng. 10(7), 213–222 (2015)
6. Verbeke, W., Wouter, V., David, M., Christophe, M., Bart, B.: Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38(3), 2354–2364 (2011)
7. Tsai, C.F., Chih-Fong, T., Yu-Hsin, L.: Customer churn prediction by hybrid neural networks. Expert Syst. Appl. 36(10), 12547–12553 (2009)
8. Vafeiadis, T., Diamantaras, K.I., Sarigiannidis, G., Chatzisavvas, K.C.: A comparison of machine learning techniques for customer churn prediction. Simul. Model. Pract. Theor. 55, 1–9 (2015)
9. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
10. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
11. Candel, A., Parmar, V., LeDell, E., Arora, A.: Deep learning with H2O (2015)
12. Hinton, G.E., Osindero, S., Teh, W.Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
13. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
14. LeCun, Y., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 9–50. Springer, Heidelberg (1998). doi:10.1007/3-540-49430-8_2
15. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)