Data mining and knowledge discovery in databases
Communications of the ACM
Neural network applications in business: a review and analysis of the literature (1988-95)
Decision Support Systems
ACM Computing Surveys (CSUR)
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The design and validation of a hybrid information system for the auditor's going concern decision
Journal of Management Information Systems - Special section: Managing virtual workplaces and teleworking with information technology
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Personalised online sales using web usage data mining
Computers in Industry
Expert Systems with Applications: An International Journal
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
Time-varying effects in the analysis of customer loyalty: A case study in insurance
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
International Journal of Information Retrieval Research
Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines
Expert Systems with Applications: An International Journal
A hybrid machine learning method and its application in municipal waste prediction
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Mobile phone customer retention strategies and Chinese e-commerce
Electronic Commerce Research and Applications
Hi-index | 12.06 |
As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN+ANN) and SOM combined with ANN (SOM+ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN+ANN hybrid model significantly performs better than the SOM+ANN hybrid model and the ANN baseline model.