Integrated Learning-Based SME Credit Rating

Limei Wang; Zhe Wang; Yu Hu; Tian Bai

doi:10.4236/jss.2014.24036

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JSS> Vol.2 No.4, April 2014

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Integrated Learning-Based SME Credit Rating ()

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DOI: 10.4236/jss.2014.24036 4,479 Downloads 5,784 Views Citations

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Limei Wang, Zhe Wang, Yu Hu, Tian Bai

Affiliation(s)

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

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

Cite this paper

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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