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

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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 classiffication 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.
Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
Issue Date
2014-09
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, v.13, no.5, pp.1013 - 1027

ISSN
0219-6220
DOI
10.1142/S0219622014500680
URI
http://hdl.handle.net/10203/192680
Appears in Collection
MT-Journal Papers(저널논문)
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