Abstract
The problem of credit card default prediction is important in finance and electronic commerce, thus it has been attracting more and more attention. Generally, the existing research work on credit card default prediction directly applies a classification model to the historical data and train a predictor, but rarely deeply explores the data. In this paper, we research the problem of credit card default prediction in an unconventional way. First, we study the records of consumption by credit card from the perspective of network to uncover the relationships between features and the ones between features and label. Second, based on the network structure we propose a new feature selection algorithm named as NSFSA. Finally, we apply the NSFSA to five machine learning models to train predictors over the real dataset of consumption records by credit card, and also compare with four existing feature selection algorithms. Experimental results show that the proposed NSFSA performs excellently, which demonstrates the potentials of our way to research the credit card default problem.
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This work is supported by Natural Science Foundation of China under Grant No. 61802034.
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Hu, Y., Ren, Y., Wang, Q. (2019). A Feature Selection Based on Network Structure for Credit Card Default Prediction. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_20
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