Abstract
Study on high-precision credit scoring model has become a challenging issue. In this paper, a new credit scoring model is proposed. We model the users and their relationship as a weighted undirected graph, and then the credit scoring problem is reduced to a prediction problem of signal on graph. The new model utilizes both the information of the unlabeled samples and the location information of the samples in the feature space, and thus achieves an excellent predictive performance. The experimental results on the open UCI German credit dataset are compared with those of seven classical models that the prediction performance of the proposed model is significantly better than that of the reference models. The Friedman test indicate that the experimental results have a high confidence level.
Supported by National Natural Science Foundations of China (Nos. 11771458, 11431015, 11601346), and Guangdong Province Key grant (No. 2016B030307003).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Henley, W.E., Hand, D.J.: Construction of a k-nearest neighbor credit-scoring system. IMA J. Manag. Math. 8(4), 305–321 (1997)
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6), 627–635 (2003)
Brown, I., Mues, C.: An experimental comparison of classification algorithms for imbalanced credit scoring datasets. Expert Syst. Appl. 39(3), 3446–3453 (2012)
Arminger, G., Enache, D., Bonne, T.: Analyzing credit risk data: a comparison of logistic discrimination, classification tree analysis, and feed forward networks. Comput. Stat. 12, 293–310 (1997)
Lessmann, S., Baesens, B., Seow, H.V.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 1–32 (2015)
West, D.: Neural network credit scoring models. Comput. Oper. Res. 27(11–12), 1131–1152 (2002)
Baesens, B., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6), 627–635 (2003)
Nani, L., Lumini, A.: An experimental comparison of ensemble classifiers for bankruptcy prediction and credit scoring. Expert Syst. Appl. 36(2), 3028–3033 (2009)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-supervised Learning. MIT Press, Cambridge (2013)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)
Liu, X., Zhai, D., Zhao, D., Zhai, G., Gao, W.: Progressive image denoising through hybrid graph Laplacian regularization: a unified framework. IEEE Trans. Image Process. 23(4), 1491–1503 (2014)
Shuman, D., Narang, S., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high- dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)
Sandryhaila, A., Moura, J.M.F.: Discrete signal processing on graphs. IEEE Trans. Signal Process. 61(7), 1644–1656 (2013)
Dong, X., Thanou, D., Frossard, P., Vandergheynst, P.: Laplacian matrix learning for smooth graph signal representation. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3736–3740 (2015)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, Z., Zhang, Q., Zhou, F., Yang, L. (2020). A New Credit Scoring Model Based on Prediction of Signal on Graph. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-59830-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59829-7
Online ISBN: 978-3-030-59830-3
eBook Packages: Computer ScienceComputer Science (R0)