Abstract
Internet loan business has received extensive attentions recently. How to provide lenders with accurate credit scoring profiles of borrowers becomes a challenge due to the tremendous amount of loan requests and the limited information of borrowers. However, existing approaches are not suitable to Internet loan business due to the unique features of individual credit data. In this paper, we propose a unified data mining framework consisting of feature transformation, feature selection and hybrid model to solve the above challenges. Extensive experiment results on realistic datasets show that our proposed framework is an effective solution.
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References
Angelini, E., di Tollo, G., Roli, A.: A neural network approach for credit risk evaluation. Q. Rev. Econ. Finan. 48(4), 733–755 (2008)
Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. arXiv preprint arXiv:1603.02754 (2016)
Chen, Y.W., Lin, C.J.: Combining svms with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction, pp. 315–324. Springer, Heidelberg (2006)
Gray, J.B., Fan, G.: Classification tree analysis using TARGET. Comput. Stat. Data Anal. 52(3), 1362–1372 (2008)
Hsieh, N.C., Hung, L.P.: A data driven ensemble classifier for credit scoring analysis. Expert Syst. Appl. 37(1), 534–545 (2010)
Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S.: Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis. Support Syst. 37(4), 543–558 (2004)
Koutanaei, F.N., Sajedi, H., Khanbabaei, M.: A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. J. Retail. Consum. Serv. 27, 11–23 (2015)
Lessmann, S., Baesens, B., Seow, H.V., Thomas, L.C.: Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 124–136 (2015)
Pang, S.L., Gong, J.Z.: C5. 0 classification algorithm and application on individual credit evaluation of banks. Syst. Eng. Theory Pract. 29(12), 94–104 (2009)
Wang, Y., Wang, S., Lai, K.K.: A new fuzzy support vector machine to evaluate credit risk. IEEE Trans. Fuzzy Syst. 13(6), 820–831 (2005)
Yap, B.W., Ong, S.H., Husain, N.H.M.: Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Syst. Appl. 38(10), 13274–13283 (2011)
Yu, L., Wang, S., Lai, K.K.: Credit risk assessment with a multistage neural network ensemble learning approach. Expert Syst. Appl. 34(2), 1434–1444 (2008)
Acknowledgment
The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China under (61472338), the Fundamental Research Funds for the Central Universities, and Macao Science and Technology Development Fund under Grant No. 096/2013/A3.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yang, Y., Zheng, Z., Huang, C., Li, K., Dai, HN. (2017). A Novel Hybrid Data Mining Framework for Credit Evaluation. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_2
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DOI: https://doi.org/10.1007/978-3-319-59288-6_2
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