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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Particle Swarm Optimization With Kernel Support Vector Machine For Churn Prediction In Telecommunication Industry

[Full Text]



Isabella Amali , Dr. R. Arunkumar



Churn prediction, Customer retention, Telecommunication, Optimization algorithm.



At present times, because of the challenges posed from global competitors, customer churn prediction (CCP) provides a major concern for organizations in different churns. To provide better customer retention, various CCP models have been presented. This paper presents a new CCP using hybridization of particle swarm optimization with kernel support vector machine (PSO-KSVM) in telecommunication industry. Here, PSO algorithm is used to optimize the variables of SVM namely C and σ. The validation of PSO-KSVM takes place using a benchmark dataset. The results ensured the effective outcome of the presented model over the compared methods.



[1] M. Shaw, C. Subramaniam, G. W. Tan, and M. E. Welge, “Knowledge management and data mining for marketing,” Decision Support Systems, Vol. 31, no. 1, 2001, pp. 127-137.
[2] C. P. Wei and I. T. Chiu, “Turning telecommunications call details to churn prediction: a data mining approach,” Expert Systems with Applications, Vol. 23, 2002, pp. 103-112.
[3] Xia, G.E. and Jin, W.D., 2008. Model of customer churn prediction on support vector machine. Systems Engineering-Theory & Practice, 28(1), pp.71-77.
[4] J. H. Ahn, S. P. Han, and Y. S. Lee, “Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry,” Telecommunications Policy, Vol. 30, Issues 10–11, 2006, pp. 552-568.
[5] Zhao, Y., Li, B., Li, X., Liu, W. and Ren, S., 2005, July. Customer churn prediction using improved one-class support vector machine. In International Conference on Advanced Data Mining and Applications (pp. 300-306). Springer, Berlin, Heidelberg.
[6] V. García, A. I. Marqués, and J. S. Sánchez, “Non-parametric Statistical Analysis of Machine Learning Methods for Credit Scoring,” Advances in Intelligent Systems and Computing, Volume 171, 2012, pp. 263-272.
[7] Brandusoiu, I. and Toderean, G., 2013. Churn prediction in the telecommunications sector using support vector machines. Margin, 1, p.x1.
[8] S. Chakrabarti, M. Ester, U. Fayyad, J. Gehrke, J. Han, S. Morishita, G. Piatetsky-Shapiro, and W. Wang, “Data Mining Curriculum: A Proposal,” Version 1.0, 2006.
[9] F. Gorunescu, Data Mining Concepts, Models and Techniques, Springer-Verlag Berlin Heidelberg, 2011.
[10] Amin, A., Shehzad, S., Khan, C., Ali, I. and Anwar, S., 2015. Churn prediction in telecommunication industry using rough set approach. In New trends in computational collective intelligence (pp. 83-95). Springer, Cham.
[11] http://www.sgi.com/tech/mlc/db/ (Last Access: November 30, 2017 02:00 PM).