Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine

Xi Zhao (), Yong Shi (), Jongwon Lee (), Heung Kee Kim () and Heeseok Lee ()
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Xi Zhao: School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
Yong Shi: Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China;
Jongwon Lee: Department of Digital Technology Management, School of Business Administration, Hoseo University, 12, Hoseodae-gil, Dongnam-gu, Cheonan-si, South Korea
Heung Kee Kim: Department of Global Entrepreneurship, School of Business Administration, Hoseo University, 12, Hoseodae-gil, Dongnam-gu, Cheonan-si, South Korea
Heeseok Lee: College of Business, Korea Advanced Institute Science and Technology, Dongdaemum-gu, Seoul 130-722, South Korea

International Journal of Information Technology & Decision Making (IJITDM), 2014, vol. 13, issue 05, 1013-1027

Abstract: Bank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classification technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications.

Keywords: Customer churn; maximal information coefficient; nonparallel support vector machine (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622014500680

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