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

Author

Listed:
  • Xi Zhao

    (School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
    Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China)

  • Yong Shi

    (Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China;
    College of Information Sciences and Technology, University of Nebraska at Omaha, Omaha, NE 68118, USA)

  • 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)

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.

Suggested Citation

  • Xi Zhao & Yong Shi & Jongwon Lee & Heung Kee Kim & Heeseok Lee, 2014. "Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(05), pages 1013-1027.
    • Handle: RePEc:wsi:ijitdm:v:13:y:2014:i:05:n:s0219622014500680
    DOI: 10.1142/S0219622014500680
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    References listed on IDEAS

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    1. Yong Shi, 2001. "Multiple Criteria and Multiple Constraint Levels Linear Programming:Concepts, Techniques and Applications," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 4000, February.
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    Cited by:

    1. Gerunov, Anton, 2016. "Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches," MPRA Paper 69199, University Library of Munich, Germany.
    2. Shen-Tsu Wang, 2018. "An Analysis of the Optimal Customer Clusters Using Dynamic Multi-Objective Decision," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 547-582, March.

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