Integrated Learning-Based SME Credit Rating

Limei Wang; Zhe Wang; Yu Hu; Tian Bai

doi:10.4236/jss.2014.24036

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Open Journal of Social Sciences > Vol.2 No.4, April 2014

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 PDF HTML 4,644 Downloads 5,956 Views Citations

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

Share and Cite:

Wang, L. , Wang, Z. , Hu, Y. and Bai, T. (2014) Integrated Learning-Based SME Credit Rating. Open Journal of Social Sciences, 2, 326-333. doi: 10.4236/jss.2014.24036.

Conflicts of Interest

The authors declare no conflicts of interest.

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