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| The Application of Improved Random Forest in the Telecom Customer Churn Prediction |
| DING Jun-Mei, LIU Gui-Quan, LI Hui |
| School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027 |
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Abstract An improved random forest algorithm (IRFA) is proposed to handle imbalanced classification and improve the prediction accuracy of high-value customers in telecom customer churn prediction. The node partition method for generating each tree is improved. Nodes are divided based on the life value of customers. Thus the problem of imbalanced data distribution is solved, and the accuracy of churn prediction of high-value customers is raised. IRFA is applied to customer churn prediction for a telecom company. Experimental results show that compared with other methods, the proposed algorithm has a better performance in classification and it improves the accuracy of churn prediction of high-value customers.
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Received: 18 September 2014
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2015, Vol. 28 
