To deal with the data mining problem of asymmetry misclassification cost, an innovative churn prediction method is proposed based on existing churn prediction research. This method adjusts the misclassification cost based on the C4. 5 decision tree as a baseline classifier, which can obtain the prediction model with a minimum error rate based on the assumption that all misclassifications have the same cost, to realize cost-sensitive learning. Results from customer data of a certain Chinese telecommunication company and the fact that the churners and the non-churners have different misclassification costs demonstrate that by altering the sampling ratio of churners and non-churners, this cost-sensitive learning method can considerably reduce the total misclassification cost produced by traditional classification methods. This method can also play an important role in promoting core competence of Chinese telecommunication industry.
