Abstract
The prediction of credit card default is a critical issue in business and so has been attracting more and more attention. In this paper, we focus on the research of credit card default prediction in an unconventional way. We firstly study the consumption behavior of credit card holders, and uncover an interesting pattern that the features (each feature represents one dimension of the consumption behavior) cluster into different groups. With the aim of exploring the effect of the observed pattern on the task of credit card default prediction, we further propose a feature selection algorithm. Finally, we test the proposed algorithm and four existing feature selection algorithms on four prediction models over the real dataset of credit card consumption. Experimental results show that the proposed algorithm gives the overall superior performance and spends less running time in most cases; this demonstrates the potential application of the observed pattern.
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Notes
- 1.
Community is a subset of nodes which are densely connected with each other while being separated well with other parts in the network. Community has the same meaning with cluster, without consideration of the representation of data. We use community, cluster, and group changeably in this paper.
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This work is supported by discipline construction project of modern information detection and intelligent processing technology and cultivating program of excellent innovation team of Chengdu University of Technology.
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Wang, Q., Hu, Y., Li, J. (2018). Community-Based Feature Selection for Credit Card Default Prediction. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_13
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DOI: https://doi.org/10.1007/978-3-319-72150-7_13
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