Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(140.88KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Classification of Churn and non-Churn Customers in Telecommunication Companies
Tarik Rashid
Pages - 82 - 89     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
MORE INFORMATION
KEYWORDS
Artificial Neural Network, Classification, Prediction, Dynamic Training, Telecommunication
ABSTRACT
Telecommunication is very important as it serves various activities, services of electronic systems to transmit messages via physical cables, telephones, or cell phones. The two main factors that affect the growth of telecommunications are the rapid growth of modern technology and the market demand and its competition. These two factors in return, create new technologies and products, which open a series of options and offers to customers, in order to satisfy their needs and requirements. However, one crucial problem that commercial companies in general and telecommunication in particular, suffer from is a loss of valuable customers to competitors; this is called customer churn prediction. In this paper, the dynamic training technique is introduced. The dynamic training is used to improve the prediction of performance. This technique is based on two ANN network configurations to minimise the total error of the network to predict two different classes; names churn and non-customers.
CITED BY (5)  
1 Adebiyi, S. O., Oyatoye, E. O., & Kuye, O. L. (2015). An Analytic Hierarchy Process Analysis: Application to Subscriber Retention Decisions in the Nigerian Mobile Telecommunications. International Journal of Management and Economics, 48(1), 63-83.
2 Mahajan, V., Misra, R., & Mahajan, R. (2015). Review of Data Mining Techniques for Churn Prediction in Telecom. Journal of Information and Organizational Sciences, 39(2), 183-197.
3 Wilgenbus, E. F. (2013). The file fragment classification problem: a combined neural network and linear programming discriminant model approach (Doctoral dissertation, North-West University).
4 Saravanan, M., & Deepika, S. M. (2012, January). AUTOMATED PROVISIONING OF CAMPAIGNS USING DATA MINING TECHNIQUES. In Proceedings of the International Conference on Data Mining (DMIN) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
5 Hashmi, N., Butt, N. A., & Iqbal, M. (2002). Customer Churn Prediction in Telecommunication.
1 Directory of Open Access Journals (DOAJ)
2 Google Scholar
3 ScientificCommons
4 Academic Index
5 CiteSeerX
6 refSeek
7 iSEEK
8 Socol@r
9 ResearchGATE
10 Bielefeld Academic Search Engine (BASE)
11 Scribd
12 WorldCat
13 slideshare
14 PdfSR
15 PDFCAST
1 Mozer M.C., Dodier R., Colagrosso M.D., Guerra-Salcedo C., “Wolniewicz R., Prodding the ROC Curve: Constrained Optimization of Classifier Performance Advances” in Neural Information Processing Systems 14, MIT Press, 2002.
2 Cedric Archaux, H. Laanya, A. Martin and A. Khenchaf. “An SVM based Churn Detector in Prepaid Mobile Telephony”, In IEEE. 2004.
3 Hollmen J., “User Profiling and Classification for Fraud Detection”. PhD Theses doctorate, University of Helsinki, 2000.
4 Taniguchi M., Haft M., Hollmen J., Tresp V. “Fraud detection in communications networks using neural and probabilistic methods”, ICCASP, Vol2, 1998, pp. 1241-1244.
5 Rosset S., Murad U., Neumann E., Idan Y., Pinkas G., “Discovery of fraud rules for telecommunications-challenges and solutions”, Proceedings ACM SIGKDD, 1999
6 H. Van Khuu, H.-KieLee, and J.-Liang Tsai. “Machine learning with neural networks and support vector machines”, 2005.
7 K. Anil and J. Mao. “Artificial neural networks: A tutorial”. IEEE ComputerSociety, 29 (3), 1996, 31 - 44.
8 T. Rashid and M-T.Kechadi, ”Effective Neural Network Approach for Energy Load Forecasting”. International Conference on Computational Intelligence, Calgary, Canada, 2005.
9 P. J. Werbos. “Backpropagation through time: What it does and how to do it”. In Proceedings of the IEEE, volume 78, 1990, pp. 1550–1560.
10 M. Boden. “A guide to recurrent neural networks and back propagation”. The DALLAS project. Report from the NUTEK-supported project AIS-8, SICS. Holst: Application of data analysis with learning systems, 2001.
11 M . Hay, “The derivation of global estimates from a confusion matrix”, International Journal of Remote Sensing, 1366-5901, Volume 9, Issue 8, 1988, pp. 1395 - 1398.
12 Zhi-Hua Zhou and Xu-Ying Liu, “On Multi-Class-Cost-Sensitive Learning”, The American Association for Artificial Intelligence. 2006.
13 L. Breiman, J. H. Friedman (1998), R. A. Olshen and C. J. Stone, “Classfication and Recognition Trees”, Wadsworth International Group, 1998, Belmont, CA.
14 U. Knoll, G. Nakhaeilzadeh, and B. Tausend, (1994), “Cost-sensitive pruning of decision trees”, in Pro, ECML 1994.
15 Shanthi Dhanushkodi, G.Sahoo , Saravanan Nallaperumal “Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke” International Journal of Biometrics and Bioinformatics (IJBB), Volume 3, Issue 1, pp: 10-18, 2009.
16 Chien-Wen Cho, Wen-Hung Chao, You-Yin Chen “A linear-discriminant-analysis-based approach to enhance the performance of fuzzy c-means clustering in spike sorting with low-SNR data” International Journal of Biometrics and Bioinformatics (IJBB) Volume 1, Issue 1, pp 1-13, 2007.
17 Aloysius George “Multi-Modal Biometrics Human Verification using LDA and DFB” International Journal of Biometrics and Bioinformatics (IJBB) Volume 2, Issue 4, pp :1-10, 2008.
Associate Professor Tarik Rashid
College of Computer Training - Ireland
tarik@cct.ie