Abstract
In order to improve the performance of a data mining model, many researchers have employed a hybrid model approach in solving a problem. There are two types of approach to build a hybrid model, i.e., the whole data approach and the segmented data approach. In this research, we present a new structure of the latter type of hybrid model, which we shall call SePI. In the SePI, input data is segmented using the performance information of the models tried in the training phase. We applied the SePI to a real customer churn problem of a Korean company that provides streaming digital music services through Internet. The result shows that the SePI outperformed any model that employed only one data mining technique such as artificial neural network, decision tree and logistic regression.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bay, S.: Nearest Neighbor Classification from Multiple Feature Subsets. Intelligent Data Analysis 3, 191–209 (1999)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Cho, S.B.: Pattern Recognition with Neural Networks Combined by Genetic Algorithm. Fuzzy Sets and Systems 103, 339–347 (1999)
Daskalaki, S., Kopanas, I., Goudara, M., Avouris, N.: Data Mining for Decision Support on Customer Insolvency in Telecommunications Business. European Journal of Operational Research 145, 239–255 (2003)
Hothorn, T., Lausen, B.: Bagging Tree Classifiers for Laser Scanning Images: A Data- and Simulation-Based Strategy. Artificial Intelligence in Medicine 27, 65–79 (2003)
Hsieh, N.C.: Hybrid Mining Approach in the Design of Credit Scoring Models. Expert Systems with Applications 28, 655–665 (2005)
Jiang, Y., Zhou, Z.H., Chen, Z.Q.: Rule Learning based on Neural Network Ensemble. In: Proceedings of the International Joint Conference on Neural Networks, Honolulu HI, pp. 1416–1420 (2002)
Kim, Y.S., Street, W.N., Menczer, F.: Optimal Ensemble Construction via Meta-evolutionary Ensembles. Expert Systems with Applications 30, 705–714 (2006)
Last, L., Kandel, A., Maimon, O.: Information-Theoretic Algorithm for Feature Selection. Pattern Recognition Letters 22, 799–811 (2001)
Opitz, D., Maclin, R.: Popular Ensemble Methods: An Emprical Study. Journal of Artificial Intelligence Research 11, 169–198 (1999)
Sexton, R.S., Sikander, N.A.: Data Mining Using a Genetic Algorithm Trained Neural Network. International Journal of Intelligent Systems in Accounting, Finance & Management 10, 201–210 (2001)
Tax, D.M.J., Breukelen, M.V., Duin, R.P.W., Kittler, J.: Combining Multiple Classifiers by Averaging or by Multiplying. Pattern Recognition 33, 1475–1485 (2000)
Wang, X., Wang, H.: Classification by Evolutionary Ensembles. Pattern Recognition 39, 595–607 (2006)
Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems and their Applications 13, 44–49 (1998)
Yeo, A.C., Smith, K.A., Willis, R.J., Brooks, M.: Clustering Technique for Risk Classification and Prediction of Claim Costs in the Automobile Insurance Industry. International Journal of Intelligent Systems in Accounting, Finance & Management 10, 39–50 (2001)
Zhang, P., Verma, B., Kumar, K.: Neural vs. Statistical Classifier in Conjunction with Genetic Algorithm based Feature Selection. Pattern Recognition Letters 26, 909–919 (2005)
Zhou, Z.H., Wu, J.X., Jiang, Y., Chen, S.F.: Genetic Algorithm based Selective Neural Network Ensemble. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle WA, vol. 2, pp. 797–802 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, J.S., Lee, J.C. (2006). Customer Churn Prediction by Hybrid Model. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_104
Download citation
DOI: https://doi.org/10.1007/11811305_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)