Abstract
This study contributes to formalize a three phase customer churn prediction technique. In the first phase, a supervised feature selection procedure is adopted to select the most relevant subset of features by laying-off the redundancy and increasing the relevance that leads to reduced and highly correlated features set. In the second phase, a knowledge based system (KBS) is built through Ripple Down Rule (RDR) learner which acquires knowledge about seen customer churn behavior and handles the problem of brittle in churn KBS through prudence analysis that will issue a prompt to the decision maker whenever a case is beyond the maintained knowledge in the knowledge database. In the final phase, a technique for Simulated Expert (SE) is proposed to evaluate the Knowledge Acquisition (KA) in KB system. Moreover, by applying the proposed approach on publicly available dataset, the results show that the proposed approach can be a worthy alternate for churn prediction in telecommunication industry.
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References
Data Set (2015), http://www.sgi.com/tech/mlc/db/ (accessed December 20, 2014)
Ahn, J.H., Han, S.P., Lee, Y.S.: Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications Policy 30(10-11), 552–568 (2006), http://www.sciencedirect.com/science/article/pii/S0308596106000760
Amin, A., Shehzad, S., Khan, C., Ali, I., Anwar, S.: Churn prediction in telecommunication industry using rough set approach. In: Camacho, D., Kim, S.W., Trawiski, B. (eds.) New Trends in Computational Collective Intelligence. SCI, vol. 572, pp. 83–95. Springer, Heidelberg (2015), http://dx.doi.org/10.1007/978-3-319-10774-5_8
Burez, J., Van den Poel, D.: CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications 32(2), 277–288 (2007), http://www.sciencedirect.com/science/article/pii/S0957417405003374
Burez, J., Van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Systems with Applications 36(3), 4626–4636 (2009), http://linkinghub.elsevier.com/retrieve/pii/S0957417408002121
Compton, P., Jansen, R.: Knowledge in context: a strategy for expert system maintenance, pp. 292–306 (March 1990), http://dl.acm.org/citation.cfm?id=89411.89756
Compton, P., Preston, P., Edwards, G., Kang, B.: Knowledge based systems that have some idea of their limits. In: Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop (1996)
Farquad, M.A.H., Ravi, V., Raju, S.B.: Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing 19, 31–40 (2014), http://www.sciencedirect.com/science/article/pii/S1568494614000507
Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. Journal of Intelligent Information Systems 5(3), 211–228 (1995), http://dl.acm.org/citation.cfm?id=218246.218250
Hadden, J., Tiwari, A., Roy, R., Ruta, D.: Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research 34(10), 2902–2917 (2007), http://www.sciencedirect.com/science/article/pii/S0305054805003503
Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench. In: Proceedings of ANZIIS 1994 - Australian New Zealnd Intelligent Information Systems Conference, pp. 357–361. IEEE (1994), http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=396988
Huang, B., Kechadi, M.T., Buckley, B.: Customer churn prediction in telecommunications. Expert Systems with Applications 39(1), 1414–1425 (2012), http://dl.acm.org/citation.cfm?id=2038068.2038213
Kang, C., Pei-ji, S.: Customer Churn Prediction Based on SVM-RFE. In: 2008 International Seminar on Business and Information Management, vol. 1, pp. 306–309. IEEE (December 2008), http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=5117490
Khan, I., Usman, I., Usman, T., Rehman, G.U., Rehman, A.U.: Intelligent Churn prediction for Telecommunication Industry. International Journal of Innovation and Applied Studies 4(1), 165–170 (2013)
Kirui, C., Hong, L., Cheruiyot, W., Kirui, H.: Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining. IJCSI International Journal of Computer Science Issues 10(2), 165–172 (2013)
Lazarov, V., Capota, M.: Churn Prediction. Business Analytics Course. TUM Computer Science (2007), http://home.in.tum.de/~lazarov/files/research/papers/churn-prediction.pdf
Lin, C.S., Tzeng, G.H., Chin, Y.C.: Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Systems with Applications 38(1), 8–15 (2011), http://linkinghub.elsevier.com/retrieve/pii/S0957417410004501
Maruatona, O., Vamplew, P., Dazeley, R.: Knowledge Management and Acquisition for Intelligent Systems. Springer, Heidelberg (2012)
Maruatona, O.O., Vamplew, P., Dazeley, R.: Prudent Fraud Detection in Internet Banking. In: 2012 Third Cybercrime and Trustworthy Computing Workshop, pp. 60–65. IEEE (October 2012), http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6498429
Mozer, M.C., Wolniewicz, R., Grimes, D.B., Johnson, E., Kaushansky, H.: Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks / A Publication of the IEEE Neural Networks Council 11(3), 690–696 (2000), http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=846740
Richards, D., Compton, P.: Taking up the situated cognition challenge with ripple down rules. International Journal of Human-Computer Studies 49(6), 895–926 (1998), http://www.sciencedirect.com/science/article/pii/S1071581998902312
Saradhi, V.V., Palshikar, G.K.: Employee churn prediction. Expert Systems with Applications 38(3), 1999–2006 (2011), http://www.sciencedirect.com/science/article/pii/S0957417410007621
Scheffer, T.: Algebraic Foundation and Improved Methods of Induction of Ripple Down Rules. In: Pasific Knowledge Acquisition Workshop, Sydney, pp. 23–25 (1996)
Sharma, A., Prabin Kumar, P.: A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications 27(11), 26–31 (2011)
Soeini, R.A., Rodpysh, K.V.: Applying Data Mining to Insurance Customer Churn Management 30, 82–92 (2012)
Suznjevic, M., Stupar, I., Matijasevic, M.: MMORPG Player Behavior Model based on Player Action Categories. IEEE (2011)
Van den Poel, D., Larivière, B.: Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research 157(1), 196–217 (2004), http://www.sciencedirect.com/science/article/pii/S0377221703000699
Verbeke, W., Martens, D., Baesens, B.: Social network analysis for customer churn prediction. Applied Soft Computing 14, 431–446 (2014), http://linkinghub.elsevier.com/retrieve/pii/S1568494613003116
Verbeke, W., Martens, D., Mues, C., Baesens, B.: Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications 38(3), 2354–2364 (2011), http://www.sciencedirect.com/science/article/pii/S0957417410008067
Wolniewicz, R.H., Dodier, R.: Predicting customer behavior in telecommunications. IEEE Intelligent Systems 19(2), 50–58 (2004), http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1274911
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Amin, A., Rahim, F., Ramzan, M., Anwar, S. (2015). A Prudent Based Approach for Customer Churn Prediction. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. BDAS 2015. Communications in Computer and Information Science, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-18422-7_29
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DOI: https://doi.org/10.1007/978-3-319-18422-7_29
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