Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards

摘要

In this work, an eclectic procedure for rule extraction from Support Vector Machine is proposed, where Tree is generated using Naive Bayes Tree (NBTree) resulting in the SVM+NBTree hybrid. The data set analyzed in this paper is about churn prediction in bank credit cards and is obtained from Business Intelligence Cup 2004. The data set under consideration is highly unbalanced with 93.11% loyal and 6.89% churned customers. Since identifying churner is of paramount importance from business perspective, sensitivity of classification model is more critical. Using the available, original unbalanced data only, we observed that the proposed hybrid SVM+NBTree yielded the best sensitivity compared to other classifiers.