Using PCA to predict customer churn in telecommunication dataset

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

Volumn 6441 LNAI, Issue PART 2, 2010, Pages 326-335

  Sato, T ^a   Huang, B Q ^a   Huang, Y ^a   Kechadi, M T ^a   Buckley, B ^b  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b Eircom Limited   (Ireland)

Author keywords

PCA; predict potential churners; telecommunication dataset

Indexed keywords

DATA SETS; EIGENVALUES; IMBALANCE DISTRIBUTIONS; LOGISTIC REGRESSIONS; NAIVE BAYES; PCA; PREDICT POTENTIAL CHURNERS; PREDICTION MODEL; PREDICTION TASKS; PRINCIPAL COMPONENTS; REAL-WORLD; CHURN PREDICTIONS; CUSTOMER CHURNS;

CLASSIFIERS; DATA MINING; DECISION TREES; EIGENVALUES AND EIGENFUNCTIONS; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; TELECOMMUNICATION; FORECASTING;

FORECASTING; DATA MINING;

EID: 78650192163     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17313-4_32     Document Type: Conference Paper

References (15)

1. Luo, B., Shao, P., Liu, J.: Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service. In: International Conference on Service Systems and Service Management, pp. 1-5 (2007)
2. Au, W., Chan, C.C., Yao, X.: A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation 7, 532-545 (2003)
3. Bäck, T.: Oxford Univeristy Press. ch.2. Oxford Univeristy Press, Oxford (1996)
4. Bradley, A.P.: The Use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145-1159 (1997)
5. Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. Newsl. 6, 1-6 (2004)
6. Coussement, K., Van den Poel, D.: hurn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications 34, 313-327 (2008)
7. Domingos, P.: MetaCost: A general method for making classifiers cost sensitive. In: The 5th International Conference on Knowledge Discovery and Data Mining, pp. 155-164 (1999)
8. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers, Dordrecht (1989)
9. Hadden, J., Tiwari, A., Roy, R., Ruta, D.: Churn Prediction: Does Technology Matter? International Journal of Intelligent Technology 1 (2006)
10. Huang, B.Q., Kechadi, M.T., Buckley, B.: Customer Churn Prediction for Broadband Internet Services. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 229-243. Springer, Heidelberg (2009)
11. Jolliffe, I.T.: Principal Components Analysis. Springer, New York (1986)
12. Kim, H.S., Yoon, C.H.: Determinants of subscriber churn and customer loyalty in Korean mobile telephony market. Telecommunications Policy 28, 751-765 (2004)
13. Oja, E.: Simplified neuron model as a principal component analyzer. Journal of Mathematical Biology 15, 267-273 (1982)
14. Wei, C., Chiu, I.: Turning telecommunications call details to churn prediction: a data mining approach. Expert Systems with Applications 23, 103-112 (2002)
15. Yen, S.J., Lee, Y.S.: Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications 36 (2009)