Integrated Learning-Based SME Credit Rating

Limei Wang, Zhe Wang, Yu Hu, Tian Bai

College of Computer Science and Technology, Jilin University, Changchun, China.

DOI: 10.4236/jss.2014.24036                

Abstract

SME Credit rating index system becomes a significant research topic in recent years. So many researches have focused on this topic. However, the existing researches are only focused on one aspect of the SME Credit Rating problem. In order to resolve this problem, in this paper, we use the idea of ensemble learning, which integrated several basic machine learning algorithms to improve the learning result. Through further amendments, we build a set of SME corporate credit evaluation models which have higher forecast accuracy and stronger anti-jamming capability. Finally, we prove the effectiveness of our model through carrying out a set of experiments.

Keywords

SME; Ensemble Learning; Credit Rating

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Li, X.F. and Li, J. (2003) Data Mining and Knowledge Discovery. Higher Education Press, Beijing.
[2]	Lin, X.-J. (2009) The Design of Medical Image Classification Based on Ensemble Leaning. Computer Knowledge and Technology, 5, 9515-9517.
[3]	Lu, Y. (2012) Research on the Credit Rating of SME. Anhui University, Hefei.
[4]	Liu, Y.L. and Liu, S.L. (2013) Research on Credit Risk Identification of Small Enterprises Based on Supply Chain Finance. Financial Market.
[5]	Bao, S.X. and Yin, Y.F. (2009) Credit Evaluation and Empirical Analysis of SME. Science & Technology Progress and Polic, 26, 143-148.
[6]	Gao, F. (2010) Research on the Credit Rating System of Chinese High Tech SMEs. Hebei University, Baoding.
[7]	Ji, H., Cao, Z.C. and Xie, W. (2012) Research Based on the Growth Theory of Small and Medium Enterprises in Technological Innovation Ability. Scientific Research.
[8]	Wang, C.F. and Wang, H.H. (2010) Summary of Enterprise Credit Rating Index System Research. Financial Forum, 8.
[9]	Lin, C.-F. and Wang, S.-D. (2002) Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks, 13, 464-471.
[10]	Wang, Y.Q., Wang, S.Y. and Lai, K.K. (2005) A New Fuzzy Support Vector Machine to Evaluate Credit Risk. IEEE Transactions on Fuzzy System, 13, 820-831.
[11]	Lin, J.X., Luo, W.Q. and Pang, S.L. (2005) Bayes Discriminant Credit Evaluation Model and Its Application. Statistics and Decision.
[12]	Ye, Z.X. and Yu, M.J. (2006) Genetic Algorithm and Classification Tree Method Based Credit Rating Method. Journal of Systems Engineering.
[13]	Pang, S.-L., Li, R.-Z. and Xu, J.-M. (2005) Application of BP Algorithm in Credit Risk Analysis. Control Theory and Applications.
[14]	Han, J.W. and Kamber, M. (2005) Data Mining Concepts and Techniques. (Fan, M. and Meng, X.F., Trans.), Mechanical Industry Press, Beijing.