The nature of statistical learning theory
The nature of statistical learning theory
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Customer Churn Prediction for Broadband Internet Services
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Monitoring and backtesting churn models
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
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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Customer Churn Prediction is an increasingly pressing issue in today's ever-competitive commercial arena. Although there are several researches in churn prediction, but the accuracy rate, which is very important to business, is not high enough. Recently, Support Vector Machines (SVMs), based on statistical learning theory, are gaining applications in the areas of data mining, machine learning, computer vision and pattern recognition because of high accuracy and good generalization capability. But there has no report about using SVM to Customer Churn Prediction. According to churn data set characteristic, the number of negative examples is very small, we introduce an improved one-class SVM. And we have tested our method on the wireless industry customer churn data set. Our method has been shown to perform very well compared with other traditional methods, ANN, Decision Tree, and Naïve Bays.