Abstract
As the rapid growth of personal credit business, we have always been seeking to establish an effective risk assessment model to achieve low costs and better accuracy of decision-making. Over the past few years, the so-called combined algorithms have appeared in many fields, but they are always useless in the field of individual credit risk assessment. So we constructed a practical method based on combined algorithms, and we tested it empirically. The result shows that the application of the method can achieve better accuracy than the BP neural network.
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References
Xiangyang, X., Jike, G.: Research on Personal Credit Scoring Model based on Clustering. Financial Electronics 9, 229–231 (2006)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transaction on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Breiman, L.: Bagging predictors. Machine Learning 26, 123–140 (1996)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36, 105–139 (1999)
Schapire, R.: The strength of weak learns ability. Machine Learning 5, 197–227 (1990)
Drucker, H., Cortes, C., Jackel, L.D., Lecun, Y., Vapkin, V.: Boosting and other ensemble methods. Neural Computation 6, 1289–1301 (1994)
Freund, Y., Schapire, R.: A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computing and Systems 55, 119–139 (1996)
Wolpert, D.: Stacked generalization. Neural Networks 5, 241–259 (1992)
Twala, B.: Multiple classifier application to credit risk assessment. Expert Systems with Applications 37, 3326–3336 (2010)
Jiuqing, H.: System Engineering. China Statistical Publishing House, Beijing (1999)
Yuanzheng, W., Yajing, X.: Cause of SAS software applications and statistics. Mechanical Industry Publishing House, Beijing (2007)
Bei, H.: Research on credit card approval models based on data mining technology. Computer Engineering and Design 6, 2989–2991 (2008)
Everitt, B.: Cluster analysis. Halsted-Wiley, New York (1974)
Hegazy, Y.A., Mayne, P.W.: Objective site characterization using clustering of piezocone data. Geotechnical Engineering Division 128, 986–996 (2002)
Guha, S., Rastogi, R., Shim, K.: Cure: an efficient clustering method for large databases. In: Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (DIGMOD 1998), Seattle, WA, pp. 73–84 (June 1998)
Jiajun, L., Yaya, L.: The improvement of the customer credit rating Based on subjective default. Financial forum 3, 26–30 (2008)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California School of Information and Computer Science, Irvine (2007)
Abdou, H., Pointon, J., Elmasry, A.: Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems and Applications 35, 1275–1292 (2008)
Khashman, A.: Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications (2010)
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Li, J., Qin, L., Zhao, J. (2010). The Construction of an Individual Credit Risk Assessment Method: Based on the Combination Algorithms. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_16
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DOI: https://doi.org/10.1007/978-3-642-16167-4_16
Publisher Name: Springer, Berlin, Heidelberg
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