Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A composite approach to inducing knowledge for expert systems design
Management Science
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An extended support vector machine forecasting framework for customer churn in e-commerce
Expert Systems with Applications: An International Journal
GA-based neural network for energy recovery system of the electric motorcycle
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
Expert Systems with Applications: An International Journal
Churn prediction in new users of Yahoo! answers
Proceedings of the 21st international conference companion on World Wide Web
Computers and Electronics in Agriculture
International Journal of Information Retrieval Research
Mobile phone customer retention strategies and Chinese e-commerce
Electronic Commerce Research and Applications
A misclassification cost risk bound based on hybrid particle swarm optimization heuristic
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Marketing research suggests that it is more expensive to recruit a new customer than to retain an existing customer. In order to retain existing customers, academics and practitioners have developed churn prediction models to effectively manage customer churn. In this paper, we propose two genetic-algorithm (GA) based neural network (NN) models to predict customer churn in subscription of wireless services. Our first GA based NN model uses a cross entropy based criterion to predict customer churn, and our second GA based NN model attempts to directly maximize the prediction accuracy of customer churn. Using real-world cellular wireless services dataset and three different sizes of NNs, we compare the two GA based NN models with a statistical z-score model using several model evaluation criteria, which include prediction accuracy, top 10% decile lift and area under receiver operating characteristics (ROC) curve. The results of our experiments indicate that both GA based NN models outperform the statistical z-score model on all performance criteria. Further, we observe that medium sized NNs perform best and the cross entropy based criterion may be more resistant to overfitting outliers in training dataset.