An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk

Yan Feng Guo; Na Sun; Yuan Yao

doi:10.4028/www.scientific.net/AMR.271-273.1286

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An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 271-273An Ensemble Learning Model Based on SOM-SVM Model...

An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk

Abstract:

Credit risk problem is an essential problem in financial management area. People usually employ personal credit scoring to avoid financial risk problem. Although many methods have been proposed for evaluating the personal credit scoring and obtained good effects, most of these methods were called single model types, which would be disturbed by model self-parameter, data noise and other external factors. In order to overcome the weakness of single model, we believe one of best ways is to construct an ensemble model. In this paper, we proposed a new style of ensemble model and employed two public credit datasets to certify the validity of our ensemble model. The experimental result shows that the ensemble SOM-SVM model can overcome the single model weakness and improve the accuracy of classification, which is good for constructing a better credit scoring system in future.

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Periodical:

Advanced Materials Research (Volumes 271-273)

Pages:

1286-1290

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.271-273.1286

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Online since:

July 2011

Authors:

Yan Feng Guo, Na Sun, Yuan Yao

Keywords:

Credit Risk, Ensemble Model, Self-Organizing Maps, Support Vector Machine (SVM)

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References

[1] E. Çomak and A. Arslan: Expert Systems with Applications Vol. 35 (2008), pp.564-568.

Google Scholar

[2] L. Yu, S. Wang and K. K. Lai: European Journal of Operational Research Vol. 195 (2009), pp.942-959.

Google Scholar

[3] J.J. Huang, G.H. Tzeng and C.S. Ong: Applied Mathematics and Computation Vol. 174 (2006), pp.1039-1053.

Google Scholar

[4] F. Hoffmann, B. Baesens, C. Mues, T. Van Gestel and J. Vanthienen: European Journal of Operational Research Vol. 177 (2007), pp.540-555.

DOI: 10.1016/j.ejor.2005.09.044

Google Scholar

[5] L. FENG, Y. YAO and B. JIN: Journal of Computational Information System Vol. 6 (2010), pp.3567-3574.

Google Scholar

[6] T.C. Tang and L.C. Chi: Expert Systems with Applications Vol. 28 (2005), pp.547-556.

Google Scholar

[7] G. Wang, J. Hao, J. Ma and H. Jiang: Expert Systems with Applications Vol. 38 (2011), pp.223-230.

Google Scholar

[8] L. Yu, W. Yue, S. Wang and K. K. Lai: Expert Systems with Applications Vol. 37 (2010), pp.1351-1360.

Google Scholar

[9] V. Ranaee, A. Ebrahimzadeh and R. Ghaderi: ISA Transactions Vol. 49 (2010), pp.577-86.

Google Scholar

[10] L. Zhou, K. K. Lai and L. Yu: Expert Systems with Applications Vol. 37 (2010), pp.127-133.

Google Scholar

[11] C. Cortes and V. Vapnik: Machine learning Vol. 20 (1995), pp.273-297.

Google Scholar

[12] T. Kohonen: Proceedings of the IEEE Vol. 78 (1990), pp.1464-1480.

Google Scholar